摘要

1. Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Chong Chen, Min Zhang, Yongfeng Zhang, Weizhi Ma, Yiqun Liu, Shaoping Ma
Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.

2. Question-Driven Purchasing Propensity Analysis for Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Long Chen, Ziyu Guan, Qibin Xu, Qiong Zhang, Huan Sun, Guangyue Lu, Deng Cai
Merchants of e-commerce Websites expect recommender systems to entice more consumption which is highly correlated with the customers' purchasing propensity. However, most existing recommender systems focus on customers' general preference rather than purchasing propensity often governed by instant demands which we deem to be well conveyed by the questions asked by customers. A typical recommendation scenario is: Bob wants to buy a cell phone which can play the game PUBG. He is interested in HUAWEI P20 and asks “can PUBG run smoothly on this phone?” under it. Then our system will be triggered to recommend the most eligible cell phones to him. Intuitively, diverse user questions could probably be addressed in reviews written by other users who have similar concerns. To address this recommendation problem, we propose a novel Question-Driven Attentive Neural Network (QDANN) to assess the instant demands of questioners and the eligibility of products based on user generated reviews, and do recommendation accordingly. Without supervision, QDANN can well exploit reviews to achieve this goal. The attention mechanisms can be used to provide explanations for recommendations. We evaluate QDANN in three domains of Taobao. The results show the efficacy of our method and its superiority over baseline methods.

3. Leveraging Title-Abstract Attentive Semantics for Paper Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Guibing Guo, Bowei Chen, Xiaoyan Zhang, Zhirong Liu, Zhenhua Dong, Xiuqiang He
Paper recommendation is a research topic to provide users with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring their semantic relationship. In this paper, we regard the abstract as a sequence of sentences, and propose a two-level attentive neural network to capture: (1) the ability of each word within a sentence to reflect if it is semantically close to the words within the title. (2) the extent of each sentence in the abstract relative to the title, which is often a good summarization of the abstract document. Specifically, we propose a Long-Short Term Memory (LSTM) network with attention to learn the representation of sentences, and integrate a Gated Recurrent Unit (GRU) network with a memory network to learn the long-term sequential sentence patterns of interacted papers for both user and item (paper) modeling. We conduct extensive experiments on two real datasets, and show that our approach outperforms other state-of-the-art approaches in terms of accuracy.

4. An Attentional Recurrent Neural Network for Personalized Next Location Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Qing Guo, Zhu Sun, Jie Zhang, Yin-Leng Theng
Most existing studies on next location recommendation propose to model the sequential regularity of check-in sequences, but suffer from the severe data sparsity issue where most locations have fewer than five following locations. To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). In particular, we first design a meta-path based random walk over a novel knowledge graph to discover location neighbors based on heterogeneous factors. A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. Meanwhile, the transition regularities of the discovered neighbors are integrated via the attention mechanism, which seamlessly cooperates with the sequential regularity as a unified recurrent framework. Experimental results on multiple real-world datasets demonstrate that ARNN outperforms state-of-the-art methods.

5. Fair Updates in Two-Sided Market Platforms: On Incrementally Updating Recommendations [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Gourab K. Patro, Abhijnan Chakraborty, Niloy Ganguly, Krishna P. Gummadi
Major online platforms today can be thought of as two-sided markets with producers and customers of goods and services. There have been concerns that over-emphasis on customer satisfaction by the platforms may affect the well-being of the producers. To counter such issues, few recent works have attempted to incorporate fairness for the producers. However, these studies have overlooked an important issue in such platforms -- to supposedly improve customer utility, the underlying algorithms are frequently updated, causing abrupt changes in the exposure of producers. In this work, we focus on the fairness issues arising out of such frequent updates, and argue for incremental updates of the platform algorithms so that the producers have enough time to adjust (both logistically and mentally) to the change. However, naive incremental updates may become unfair to the customers. Thus focusing on recommendations deployed on two-sided platforms, we formulate an ILP based online optimization to deploy changes incrementally in η steps, where we can ensure smooth transition of the exposure of items while guaranteeing a minimum utility for every customer. Evaluations over multiple real world datasets show that our proposed mechanism for platform updates can be efficient and fair to both the producers and the customers in two-sided platforms.

6. Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Dilruk Perera, Roger Zimmermann
The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users. Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network baselines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.

7. PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Tiancheng Shen, Jia Jia, Yan Li, Yihui Ma, Yaohua Bu, Hanjie Wang, Bo Chen, Tat-Seng Chua, Wendy Hall
With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.

8. Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, Hongzhi Yin
Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

9. Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Yang Xu, Lei Zhu, Zhiyong Cheng, Jingjing Li, Jiande Sun
Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly relies on the user-item interactions and single specific content feature. When the interaction history or the content feature is unavailable (the cold-start problem), their performance will be seriously deteriorated. 2) Existing methods learn the hash codes with relaxed optimization or adopt discrete coordinate descent to directly solve binary hash codes, which results in significant quantization loss or consumes considerable computation time. In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. Specifically, a low-rank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary yet informative hash codes by fully exploiting their complementarity. Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations. Experiments on two public recommendation datasets demonstrate that MFDCF outperforms the state-of-the-arts on various aspects.

10. Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data [PDF] 返回目录
AAAI 2020. AAAI Technical Track: AI and the Web
Mengyu Zhou, Wang Tao, Pengxin Ji, Han Shi, Dongmei Zhang
Given a table of multi-dimensional data, what analyses would human create to extract information from it? From scientific exploration to business intelligence (BI), this is a key problem to solve towards automation of knowledge discovery and decision making. In this paper, we propose Table2Analysis to learn commonly conducted analysis patterns from large amount of (table, analysis) pairs, and recommend analyses for any given table even not seen before. Multi-dimensional data as input challenges existing model architectures and training techniques to fulfill the task. Based on deep Q-learning with heuristic search, Table2Analysis does table to sequence generation, with each sequence encoding an analysis. Table2Analysis has 0.78 recall at top-5 and 0.65 recall at top-1 in our evaluation against a large scale spreadsheet corpus on the PivotTable recommendation task.

11. Enhancing Personalized Trip Recommendation with Attractive Routes [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Applications
Jiqing Gu, Chao Song, Wenjun Jiang, Xiaomin Wang, Ming Liu
Personalized trip recommendation tries to recommend a sequence of point of interests (POIs) for a user. Most of existing studies search POIs only according to the popularity of POIs themselves. In fact, the routes among the POIs also have attractions to visitors, and some of these routes have high popularity. We term this kind of route as Attractive Route (AR), which brings extra user experience. In this paper, we study the attractive routes to improve personalized trip recommendation. To deal with the challenges of discovery and evaluation of ARs, we propose a personalized Trip Recommender with POIs and Attractive Route (TRAR). It discovers the attractive routes based on the popularity and the Gini coefficient of POIs, then it utilizes a gravity model in a category space to estimate the rating scores and preferences of the attractive routes. Based on that, TRAR recommends a trip with ARs to maximize user experience and leverage the tradeoff between the time cost and the user experience. The experimental results show the superiority of TRAR compared with other state-of-the-art methods.

12. Towards Hands-Free Visual Dialog Interactive Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Applications
Tong Yu, Yilin Shen, Hongxia Jin
With the recent advances of multimodal interactive recommendations, the users are able to express their preference by natural language feedback to the item images, to find the desired items. However, the existing systems either retrieve only one item or require the user to specify (e.g., by click or touch) the commented items from a list of recommendations in each user interaction. As a result, the users are not hands-free and the recommendations may be impractical. We propose a hands-free visual dialog recommender system to interactively recommend a list of items. At each time, the system shows a list of items with visual appearance. The user can comment on the list in natural language, to describe the desired features they further want. With these multimodal data, the system chooses another list of items to recommend. To understand the user preference from these multimodal data, we develop neural network models which identify the described items among the list and further predict the desired attributes. To achieve efficient interactive recommendations, we leverage the inferred user preference and further develop a novel bandit algorithm. Specifically, to avoid the system exploring more than needed, the desired attributes are utilized to reduce the exploration space. More importantly, to achieve sample efficient learning in this hands-free setting, we derive additional samples from the user's relative preference expressed in natural language and design a pairwise logistic loss in bandit learning. Our bandit model is jointly updated by the pairwise logistic loss on the additional samples derived from natural language feedback and the traditional logistic loss. The empirical results show that the probability of finding the desired items by our system is about 3 times as high as that by the traditional interactive recommenders, after a few user interactions.

13. Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Machine Learning
Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen
Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user's preference; or adaptively infer personalized confidence weights but suffer from low efficiency.

14. Sequential Recommendation with Relation-Aware Kernelized Self-Attention [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Machine Learning
Mingi Ji, Weonyoung Joo, Kyungwoo Song, Yoon-Yeong Kim, Il-Chul Moon
Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Transformer is a deterministic measure without relation-awareness. Therefore, we introduce a latent space to the self-attention, and the latent space models the recommendation context from relation as a multivariate skew-normal distribution with a kernelized covariance matrix from co-occurrences, item characteristics, and user information. This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics. We experimented RKSA over the benchmark datasets, and RKSA shows significant improvements compared to the recent baseline models. Also, RKSA were able to produce a latent space model that answers the reasons for recommendation.

15. Stochastically Robust Personalized Ranking for LSH Recommendation Retrieval [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Machine Learning
Dung D. Le, Hady W. Lauw
Locality Sensitive Hashing (LSH) has become one of the most commonly used approximate nearest neighbor search techniques to avoid the prohibitive cost of scanning through all data points. For recommender systems, LSH achieves efficient recommendation retrieval by encoding user and item vectors into binary hash codes, reducing the cost of exhaustively examining all the item vectors to identify the top-k items. However, conventional matrix factorization models may suffer from performance degeneration caused by randomly-drawn LSH hash functions, directly affecting the ultimate quality of the recommendations. In this paper, we propose a framework named øurmodel, which factors in the stochasticity of LSH hash functions when learning real-valued user and item latent vectors, eventually improving the recommendation accuracy after LSH indexing. Experiments on publicly available datasets show that the proposed framework not only effectively learns user's preferences for prediction, but also achieves high compatibility with LSH stochasticity, producing superior post-LSH indexing performances as compared to state-of-the-art baselines.

16. Symmetric Metric Learning with Adaptive Margin for Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Machine Learning
Mingming Li, Shuai Zhang, Fuqing Zhu, Wanhui Qian, Liangjun Zang, Jizhong Han, Songlin Hu
Metric learning based methods have attracted extensive interests in recommender systems. Current methods take the user-centric way in metric space to ensure the distance between user and negative item to be larger than that between the current user and positive item by a fixed margin. While they ignore the relations among positive item and negative item. As a result, these two items might be positioned closely, leading to incorrect results. Meanwhile, different users usually have different preferences, the fixed margin used in those methods can not be adaptive to various user biases, and thus decreases the performance as well. To address these two problems, a novel Symmetic Metric Learning with adaptive margin (SML) is proposed. In addition to the current user-centric metric, it symmetically introduces a positive item-centric metric which maintains closer distance from positive items to user, and push the negative items away from the positive items at the same time. Moreover, the dynamically adaptive margins are well trained to mitigate the impact of bias. Experimental results on three public recommendation datasets demonstrate that SML produces a competitive performance compared with several state-of-the-art methods.

17. Diversified Interactive Recommendation with Implicit Feedback [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Machine Learning
Yong Liu, Yingtai Xiao, Qiong Wu, Chunyan Miao, Juyong Zhang, Binqiang Zhao, Haihong Tang
Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attention. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsatisfying user experiences. In this paper, we propose a novel diversified recommendation model, named Diversified Contextual Combinatorial Bandit (DC2B), for interactive recommendation with users' implicit feedback. Specifically, DC2B employs determinantal point process in the recommendation procedure to promote diversity of the recommendation results. To learn the model parameters, a Thompson sampling-type algorithm based on variational Bayesian inference is proposed. In addition, theoretical regret analysis is also provided to guarantee the performance of DC2B. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed method in balancing the recommendation accuracy and diversity.

18. Memory Augmented Graph Neural Networks for Sequential Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Machine Learning
Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates
The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may depend on the items accessed in the past. However, the substantial increase of users and items makes sequential recommender systems still face non-trivial challenges: (1) the hardness of modeling the short-term user interests; (2) the difficulty of capturing the long-term user interests; (3) the effective modeling of item co-occurrence patterns. To tackle these challenges, we propose a memory augmented graph neural network (MA-GNN) to capture both the long- and short-term user interests. Specifically, we apply a graph neural network to model the item contextual information within a short-term period and utilize a shared memory network to capture the long-range dependencies between items. In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items. We extensively evaluate our model on five real-world datasets, comparing with several state-of-the-art methods and using a variety of performance metrics. The experimental results demonstrate the effectiveness of our model for the task of Top-K sequential recommendation.

19. Attention-Guide Walk Model in Heterogeneous Information Network for Multi-Style Recommendation Explanation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Machine Learning
Xin Wang, Ying Wang, Yunzhi Ling
Explainable Recommendation aims at not only providing the recommended items to users, but also making users aware why these items are recommended. Too many interactive factors between users and items can be used to interpret the recommendation in a heterogeneous information network. However, these interactive factors are usually massive, implicit and noisy. The existing recommendation explanation approaches only consider the single explanation style, such as aspect-level or review-level. To address these issues, we propose a framework (MSRE) of generating the multi-style recommendation explanation with the attention-guide walk model on affiliation relations and interaction relations in the heterogeneous information network. Inspired by the attention mechanism, we determine the important contexts for recommendation explanation and learn joint representation of multi-style user-item interactions for enhancing recommendation performance. Constructing extensive experiments on three real-world datasets verifies the effectiveness of our framework on both recommendation performance and recommendation explanation.

20. Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Machine Learning
Xiao Xu, Fang Dong, Yanghua Li, Shaojian He, Xin Li
A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users. Two models with disjoint and hybrid payoffs are considered to characterize the phenomenon that users' preferences towards different items vary differently over time. In the disjoint payoff model, the reward of playing an arm is determined by an arm-specific preference vector, which is piecewise-stationary with asynchronous and distinct changes across different arms. An efficient learning algorithm that is adaptive to abrupt reward changes is proposed and theoretical regret analysis is provided to show that a sublinear scaling of regret in the time length T is achieved. The algorithm is further extended to a more general setting with hybrid payoffs where the reward of playing an arm is determined by both an arm-specific preference vector and a joint coefficient vector shared by all arms. Empirical experiments are conducted on real-world datasets to verify the advantages of the proposed learning algorithms against baseline ones in both settings.

21. A Knowledge-Aware Attentional Reasoning Network for Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Machine Learning
Qiannan Zhu, Xiaofei Zhou, Jia Wu, Jianlong Tan, Li Guo
Knowledge-graph-aware recommendation systems have increasingly attracted attention in both industry and academic recently. Many existing knowledge-aware recommendation methods have achieved better performance, which usually perform recommendation by reasoning on the paths between users and items in knowledge graphs. However, they ignore the users' personal clicked history sequences that can better reflect users' preferences within a period of time for recommendation. In this paper, we propose a knowledge-aware attentional reasoning network KARN that incorporates the users' clicked history sequences and path connectivity between users and items for recommendation. The proposed KARN not only develops an attention-based RNN to capture the user's history interests from the user's clicked history sequences, but also a hierarchical attentional neural network to reason on paths between users and items for inferring the potential user intents on items. Based on both user's history interest and potential intent, KARN can predict the clicking probability of the user with respective to a candidate item. We conduct experiment on Amazon review dataset, and the experimental results demonstrate the superiority and effectiveness of our proposed KARN model.

22. Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation [PDF] 返回目录
AAAI 2020. AAAI Technical Track: Natural Language Processing
Xin Dong, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Bo Zong, Dongjin Song, Yanchi Liu, Haifeng Chen, Gerard de Melo
Recently, recommender systems have been able to emit substantially improved recommendations by leveraging user-provided reviews. Existing methods typically merge all reviews of a given user (item) into a long document, and then process user and item documents in the same manner. In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous. In this work, we develop a novel neural network model that properly accounts for this important difference by means of asymmetric attentive modules. The user module learns to attend to only those signals that are relevant with respect to the target item, whereas the item module learns to extract the most salient contents with regard to properties of the item. Our multi-hierarchical paradigm accounts for the fact that neither are all reviews equally useful, nor are all sentences within each review equally pertinent. Extensive experimental results on a variety of real datasets demonstrate the effectiveness of our method.

23. Understanding Chat Messages for Sticker Recommendation in Messaging Apps [PDF] 返回目录
AAAI 2020. IAAI Technical Track: Deployed Papers
Abhishek Laddha, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa, Ankur Narang
Stickers are popularly used in messaging apps such as Hike to visually express a nuanced range of thoughts and utterances to convey exaggerated emotions. However, discovering the right sticker from a large and ever expanding pool of stickers while chatting can be cumbersome. In this paper, we describe a system for recommending stickers in real time as the user is typing based on the context of the conversation. We decompose the sticker recommendation (SR) problem into two steps. First, we predict the message that the user is likely to send in the chat. Second, we substitute the predicted message with an appropriate sticker. Majority of Hike's messages are in the form of text which is transliterated from users' native language to the Roman script. This leads to numerous orthographic variations of the same message and makes accurate message prediction challenging. To address this issue, we learn dense representations of chat messages employing character level convolution network in an unsupervised manner. We use them to cluster the messages that have the same meaning. In the subsequent steps, we predict the message cluster instead of the message. Our approach does not depend on human labelled data (except for validation), leading to fully automatic updation and tuning pipeline for the underlying models. We also propose a novel hybrid message prediction model, which can run with low latency on low-end phones that have severe computational limitations. Our described system has been deployed for more than 6 months and is being used by millions of users along with hundreds of thousands of expressive stickers.

24. Implicit Skills Extraction Using Document Embedding and Its Use in Job Recommendation [PDF] 返回目录
AAAI 2020. IAAI Technical Track: Emerging Papers
Akshay Gugnani, Hemant Misra
This paper presents a job recommender system to match resumes to job descriptions (JD), both of which are non-standard and unstructured/semi-structured in form. First, the paper proposes a combination of natural language processing (NLP) techniques for the task of skill extraction. The performance of the combined techniques on an industrial scale dataset yielded a precision and recall of 0.78 and 0.88 respectively. The paper then introduces the concept of extracting implicit skills – the skills which are not explicitly mentioned in a JD but may be implicit in the context of geography, industry or role. To mine and infer implicit skills for a JD, we find the other JDs similar to this JD. This similarity match is done in the semantic space. A Doc2Vec model is trained on 1.1 Million JDs covering several domains crawled from the web, and all the JDs are projected onto this semantic space. The skills absent in the JD but present in similar JDs are obtained, and the obtained skills are weighted using several techniques to obtain the set of final implicit skills. Finally, several similarity measures are explored to match the skills extracted from a candidate's resume to explicit and implicit skills of JDs. Empirical results for matching resumes and JDs demonstrate that the proposed approach gives a mean reciprocal rank of 0.88, an improvement of 29.4% when compared to the performance of a baseline method that uses only explicit skills.

25. Discovery News: A Generic Framework for Financial News Recommendation [PDF] 返回目录
AAAI 2020. IAAI Technical Track: Emerging Papers
Chong Wang, Lisa Kim, Grace Bang, Himani Singh, Russell Kociuba, Steven Pomerville, Xiaomo Liu
In the financial services industry, it is crucial for analysts to constantly monitor and stay informed on the latest developments of their portfolio of companies. This ensures that analysts are up-to-date in their analysis and provide highly credible and timely insights. Currently, analysts receive news alerts through manually created news alert subscriptions that are often noisy and difficult to manage. The manual review process is time-consuming and error-prone. We demonstrate Discovery News, a framework for an automated news recommender system for financial analysis at S&P's Global Ratings. This system includes the automated ingestion, relevancy, clustering, and ranking of news. The proposed framework is adaptable to any form of input news data and can seamlessly integrate with other data used for analysis like financial data.

26. Towards an Integrative Educational Recommender for Lifelong Learners [PDF] 返回目录
AAAI 2020. Student Abstract Track
Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents unique challenges, requiring scalable and transparent models that can account for learner knowledge and content novelty simultaneously, while also retaining accurate learners representations for long periods of time. We attempt to build a novel educational recommender, that relies on an integrative approach combining multiple drivers of learners engagement. Our first step towards this goal is TrueLearn, which models content novelty and background knowledge of learners and achieves promising performance while retaining a human interpretable learner model.

27. Deep Ranking for Style-Aware Room Recommendations [PDF] 返回目录
AAAI 2020. Student Abstract Track
Ilkay Yildiz, Esra Ataer Cansizoglu, Hantian Liu, Peter B. Golbus, Ozan Tezcan, Jae-Woo Choi
We present a deep learning based room image retrieval framework that is based on style understanding. Given a dataset of room images labeled by interior design experts, we map the noisy style labels to comparison labels. Our framework learns the style spectrum of each image from the generated comparisons and makes significantly more accurate recommendations compared to discrete classification baselines.

28. Fine-grained Interest Matching for Neural News Recommendation [PDF] 返回目录
ACL 2020.
Heyuan Wang, Fangzhao Wu, Zheng Liu, Xing Xie
Personalized news recommendation is a critical technology to improve users’ online news reading experience. The core of news recommendation is accurate matching between user’s interests and candidate news. The same user usually has diverse interests that are reflected in different news she has browsed. Meanwhile, important semantic features of news are implied in text segments of different granularities. Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation. In this paper, we propose FIM, a Fine-grained Interest Matching method for neural news recommendation. Instead of aggregating user’s all historical browsed news into a unified vector, we hierarchically construct multi-level representations for each news via stacked dilated convolutions. Then we perform fine-grained matching between segment pairs of each browsed news and the candidate news at each semantic level. High-order salient signals are then identified by resembling the hierarchy of image recognition for final click prediction. Extensive experiments on a real-world dataset from MSN news validate the effectiveness of our model on news recommendation.

29. Towards Conversational Recommendation over Multi-Type Dialogs [PDF] 返回目录
ACL 2020.
Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu
We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user’s interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), where there are multiple sequential dialogs for a pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies.

30. Dynamic Online Conversation Recommendation [PDF] 返回目录
ACL 2020.
Xingshan Zeng, Jing Li, Lu Wang, Zhiming Mao, Kam-Fai Wong
Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner. Here we study dynamic online conversation recommendation, to help users engage in conversations that satisfy their evolving interests. While most prior work assumes static user interests, our model is able to capture the temporal aspects of user interests, and further handle future conversations that are unseen during training time. Concretely, we propose a neural architecture to exploit changes of user interactions and interests over time, to predict which discussions they are likely to enter. We conduct experiments on large-scale collections of Reddit conversations, and results on three subreddits show that our model significantly outperforms state-of-the-art models that make a static assumption of user interests. We further evaluate on handling “cold start”, and observe consistently better performance by our model when considering various degrees of sparsity of user’s chatting history and conversation contexts. Lastly, analyses on our model outputs indicate user interest change, explaining the advantage and efficacy of our approach.

31. MIND: A Large-scale Dataset for News Recommendation [PDF] 返回目录
ACL 2020.
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou
News recommendation is an important technique for personalized news service. Compared with product and movie recommendations which have been comprehensively studied, the research on news recommendation is much more limited, mainly due to the lack of a high-quality benchmark dataset. In this paper, we present a large-scale dataset named MIND for news recommendation. Constructed from the user click logs of Microsoft News, MIND contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. We demonstrate MIND a good testbed for news recommendation through a comparative study of several state-of-the-art news recommendation methods which are originally developed on different proprietary datasets. Our results show the performance of news recommendation highly relies on the quality of news content understanding and user interest modeling. Many natural language processing techniques such as effective text representation methods and pre-trained language models can effectively improve the performance of news recommendation. The MIND dataset will be available at https://msnews.github.io.

32. Graph Neural News Recommendation with Unsupervised Preference Disentanglement [PDF] 返回目录
ACL 2020.
Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou
With the explosion of news information, personalized news recommendation has become very important for users to quickly find their interested contents. Most existing methods usually learn the representations of users and news from news contents for recommendation. However, they seldom consider high-order connectivity underlying the user-news interactions. Moreover, existing methods failed to disentangle a user’s latent preference factors which cause her clicks on different news. In this paper, we model the user-news interactions as a bipartite graph and propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement, named GNUD. Our model can encode high-order relationships into user and news representations by information propagation along the graph. Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability. A preference regularizer is also designed to force each disentangled subspace to independently reflect an isolated preference, improving the quality of the disentangled representations. Experimental results on real-world news datasets demonstrate that our proposed model can effectively improve the performance of news recommendation and outperform state-of-the-art news recommendation methods.

33. Deep Learning-based Online Alternative Product Recommendations at Scale [PDF] 返回目录
ACL 2020. the 3rd Workshop on e-Commerce and NLP
Mingming Guo, Nian Yan, Xiquan Cui, San He Wu, Unaiza Ahsan, Rebecca West, Khalifeh Al Jadda
Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customers’ needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12% in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.

34. INSPIRED: Toward Sociable Recommendation Dialog Systems [PDF] 返回目录
EMNLP 2020. Long Paper
Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, Zhou Yu
In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge when developing a sociable recommendation dialog system, due to the lack of dialog dataset annotated with such sociable strategies. Therefore, we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs. Our analysis shows that sociable recommendation strategies, such as sharing personal opinions or communicating with encouragement, more frequently lead to successful recommendations. Based on our dataset, we train end-to-end recommendation dialog systems with and without our strategy labels. In both automatic and human evaluation, our model with strategy incorporation outperforms the baseline model. This work is a first step for building sociable recommendation dialog systems with a basis of social science theories.

35. Privacy-Preserving News Recommendation Model Learning [PDF] 返回目录
EMNLP 2020. Findings Short Paper
Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie
News recommendation aims to display news articles to users based on their personal interest. Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. In this paper, we propose a privacy-preserving method for news recommendation model training based on federated learning, where the user behavior data is locally stored on user devices. Our method can leverage the useful information in the behaviors of massive number users to train accurate news recommendation models and meanwhile remove the need of centralized storage of them. More specifically, on each user device we keep a local copy of the news recommendation model, and compute gradients of the local model based on the user behaviors in this device. The local gradients from a group of randomly selected users are uploaded to server, which are further aggregated to update the global model in the server. Since the model gradients may contain some implicit private information, we apply local differential privacy (LDP) to them before uploading for better privacy protection. The updated global model is then distributed to each user device for local model update. We repeat this process for multiple rounds. Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.

36. PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation [PDF] 返回目录
EMNLP 2020. Findings Short Paper
Guangneng Hu, Qiang Yang
Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain. Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the source domain. The transferred knowledge, however, may unintendedly leak private information of the source domain. For example, an attacker can accurately infer user demographics from their historical purchase provided by a source domain data owner. This paper addresses the above privacy-preserving issue by learning a privacy-aware neural representation by improving target performance while protecting source privacy. The key idea is to simulate the attacks during the training for protecting unseen users’ privacy in the future, modeled by an adversarial game, so that the transfer learning model becomes robust to attacks. Experiments show that the proposed PrivNet model can successfully disentangle the knowledge benefitting the transfer from leaking the privacy.

37. Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection [PDF] 返回目录
ICLR 2020.
Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, Yan Liu
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box recommender systems. In particular, we propose to interpret feature interactions from a source recommender model and explicitly encode these interactions in a target recommender model, where both source and target models are black-boxes. By not assuming the structure of the recommender system, our approach can be used in general settings. In our experiments, we focus on a prominent use of machine learning recommendation: ad-click prediction. We found that our interaction interpretations are both informative and predictive, e.g., significantly outperforming existing recommender models. What's more, the same approach to interpret interactions can provide new insights into domains even beyond recommendation, such as text and image classification.

38. Adversarial Oracular Seq2seq Learning for Sequential Recommendation [PDF] 返回目录
IJCAI 2020.
Pengyu Zhao, Tianxiao Shui, Yuanxing Zhang, Kecheng Xiao, Kaigui Bian
Recently, sequential recommendation has become a significant demand for many real-world applications, where the recommended items would be displayed to users one after another and the order of the displays influences the satisfaction of users. An extensive number of models have been developed for sequential recommendation by recommending the next items with the highest scores based on the user histories while few efforts have been made on identifying the transition dependency and behavior continuity in the recommended sequences. In this paper, we introduce the Adversarial Oracular Seq2seq learning for sequential Recommendation (AOS4Rec), which formulates the sequential recommendation as a seq2seq learning problem to portray time-varying interactions in the recommendation, and exploits the oracular learning and adversarial learning to enhance the recommendation quality. We examine the performance of AOS4Rec over RNN-based and Transformer-based recommender systems on two large datasets from real-world applications and make comparisons with state-of-the-art methods. Results indicate the accuracy and efficiency of AOS4Rec, and further analysis verifies that AOS4Rec has both robustness and practicability for real-world scenarios.

39. Memory Augmented Neural Model for Incremental Session-based Recommendation [PDF] 返回目录
IJCAI 2020.
Fei Mi, Boi Faltings
Increasing concerns with privacy have stimulated interests in Session-based Recommendation (SR) using no personal data other than what is observed in the current browser session. Existing methods are evaluated in static settings which rarely occur in real-world applications. To better address the dynamic nature of SR tasks, we study an incremental SR scenario, where new items and preferences appear continuously. We show that existing neural recommenders can be used in incremental SR scenarios with small incremental updates to alleviate computation overhead and catastrophic forgetting. More importantly, we propose a general framework called Memory Augmented Neural model (MAN). MAN augments a base neural recommender with a continuously queried and updated nonparametric memory, and the predictions from the neural and the memory components are combined through another lightweight gating network. We empirically show that MAN is well-suited for the incremental SR task, and it consistently outperforms state-oft-he-art neural and nonparametric methods. We analyze the results and demonstrate that it is particularly good at incrementally learning preferences on new and infrequent items.

40. Synthesizing Aspect-Driven Recommendation Explanations from Reviews [PDF] 返回目录
IJCAI 2020.
Trung-Hoang Le, Hady W. Lauw
Explanations help users make sense of recommendations, increasing the likelihood of adoption. Existing approaches to explainable recommendations tend to rely on rigidly standardized templates, only allowing fill-in-the-blank aspect-level sentiments. For more flexible, literate, and varied explanations that cover various aspects of interest, we propose to synthesize an explanation by selecting snippets from reviews to optimize representativeness and coherence. To fit the target user's aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of varying product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text generation.

41. Neural Tensor Model for Learning Multi-Aspect Factors in Recommender Systems [PDF] 返回目录
IJCAI 2020.
Huiyuan Chen, Jing Li
Recommender systems often involve multi-aspect factors. For example, when shopping for shoes online, consumers usually look through their images, ratings, and product's reviews before making their decisions. To learn multi-aspect factors, many context-aware models have been developed based on tensor factorizations. However, existing models assume multilinear structures in the tensor data, thus failing to capture nonlinear feature interactions. To fill this gap, we propose a novel nonlinear tensor machine, which combines deep neural networks and tensor algebra to capture nonlinear interactions among multi-aspect factors. We further consider adversarial learning to assist the training of our model. Extensive experiments demonstrate the effectiveness of the proposed model.

42. Contextualized Point-of-Interest Recommendation [PDF] 返回目录
IJCAI 2020.
Peng Han, Zhongxiao Li, Yong Liu, Peilin Zhao, Jing Li, Hao Wang, Shuo Shang
Point-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i.e., global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.

43. Deep Feedback Network for Recommendation [PDF] 返回目录
IJCAI 2020.
Ruobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, Leyu Lin
Both explicit and implicit feedbacks can reflect user opinions on items, which are essential for learning user preferences in recommendation. However, most current recommendation algorithms merely focus on implicit positive feedbacks (e.g., click), ignoring other informative user behaviors. In this paper, we aim to jointly consider explicit/implicit and positive/negative feedbacks to learn user unbiased preferences for recommendation. Specifically, we propose a novel Deep feedback network (DFN) modeling click, unclick and dislike behaviors. DFN has an internal feedback interaction component that captures fine-grained interactions between individual behaviors, and an external feedback interaction component that uses precise but relatively rare feedbacks (click/dislike) to extract useful information from rich but noisy feedbacks (unclick). In experiments, we conduct both offline and online evaluations on a real-world recommendation system WeChat Top Stories used by millions of users. The significant improvements verify the effectiveness and robustness of DFN. The source code is in https://github.com/qqxiaochongqq/DFN.

44. Learning Personalized Itemset Mapping for Cross-Domain Recommendation [PDF] 返回目录
IJCAI 2020.
Yinan Zhang, Yong Liu, Peng Han, Chunyan Miao, Lizhen Cui, Baoli Li, Haihong Tang
Cross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i.e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.

45. Intent Preference Decoupling for User Representation on Online Recommender System [PDF] 返回目录
IJCAI 2020.
Zhaoyang Liu, Haokun Chen, Fei Sun, Xu Xie, Jinyang Gao, Bolin Ding, Yanyan Shen
Accurately characterizing the user's current interest is the core of recommender systems. However, users' interests are dynamic and affected by intent factors and preference factors. The intent factors imply users' current needs and change among different visits. The preference factors are relatively stable and learned continuously over time. Existing works either resort to the sequential recommendation to model the current browsing intent and historical preference separately or just mix up these two factors during online learning. In this paper, we propose a novel learning strategy named FLIP to decouple the learning of intent and preference under the online settings. The learning of the intent is considered as a meta-learning task and fast adaptive to the current browsing; the learning of the preference is based on the calibrated user intent and constantly updated over time.We conducted experiments on two public datasets and a real-world recommender system. When equipping it with modern recommendation methods, significant improvements are demonstrated over strong baselines.

46. Collaborative Self-Attention Network for Session-based Recommendation [PDF] 返回目录
IJCAI 2020.
Anjing Luo, Pengpeng Zhao, Yanchi Liu, Fuzhen Zhuang, Deqing Wang, Jiajie Xu, Junhua Fang, Victor S. Sheng
Session-based recommendation becomes a research hotspot for its ability to make recommendations for anonymous users. However, existing session-based methods have the following limitations: (1) They either lack the capability to learn complex dependencies or focus mostly on the current session without explicitly considering collaborative information. (2) They assume that the representation of an item is static and fixed for all users at each time step. We argue that even the same item can be represented differently for different users at the same time step. To this end, we propose a novel solution, Collaborative Self-Attention Network (CoSAN) for session-based recommendation, to learn the session representation and predict the intent of the current session by investigating neighborhood sessions. Specially, we first devise a collaborative item representation by aggregating the embedding of neighborhood sessions retrieved according to each item in the current session. Then, we apply self-attention to learn long-range dependencies between collaborative items and generate collaborative session representation. Finally, each session is represented by concatenating the collaborative session representation and the embedding of the current session. Extensive experiments on two real-world datasets show that CoSAN constantly outperforms state-of-the-art methods.

47. Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability [PDF] 返回目录
IJCAI 2020.
Deng Pan, Xiangrui Li, Xin Li, Dongxiao Zhu
Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much attention from research community. However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactoryaccuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss. To evaluatethe explainability, we propose two new evaluation metrics specifically designed for aspect-level explanation using surrogate ground truth. Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. Code is available from https://github.com/pd90506/AMCF.

48. Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation [PDF] 返回目录
IJCAI 2020.
Ruobing Xie, Zhijie Qiu, Jun Rao, Yi Liu, Bo Zhang, Leyu Lin
Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https://github.com/zhijieqiu/ICAN.

49. Towards Explainable Conversational Recommendation [PDF] 返回目录
IJCAI 2020.
Zhongxia Chen, Xiting Wang, Xing Xie, Mehul Parsana, Akshay Soni, Xiang Ao, Enhong Chen
Recent studies have shown that both accuracy and explainability are important for recommendation. In this paper, we introduce explainable conversational recommendation, which enables incremental improvement of both recommendation accuracy and explanation quality through multi-turn user-model conversation. We show how the problem can be formulated, and design an incremental multi-task learning framework that enables tight collaboration between recommendation prediction, explanation generation, and user feedback integration. We also propose a multi-view feedback integration method to enable effective incremental model update. Empirical results demonstrate that our model not only consistently improves the recommendation accuracy but also generates explanations that fit user interests reflected in the feedbacks.

50. A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation [PDF] 返回目录
IJCAI 2020.
Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Xiaolin Zheng
The conventional single-target Cross-Domain Recommendation (CDR) only improves the recommendation accuracy on a target domain with the help of a source domain (with relatively richer information). In contrast, the novel dual-target CDR has been proposed to improve the recommendation accuracies on both domains simultaneously. However, dual-target CDR faces two new challenges: (1) how to generate more representative user and item embeddings, and (2) how to effectively optimize the user/item embeddings on each domain. To address these challenges, in this paper, we propose a graphical and attentional framework, called GA-DTCDR. In GA-DTCDR, we first construct two separate heterogeneous graphs based on the rating and content information from two domains to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common users learned from both domains. Both steps significantly enhance the quality of user and item embeddings and thus improve the recommendation accuracy on each domain. Extensive experiments conducted on four real-world datasets demonstrate that GA-DTCDR significantly outperforms the state-of-the-art approaches.

51. User Modeling with Click Preference and Reading Satisfaction for News Recommendation [PDF] 返回目录
IJCAI 2020.
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang
Modeling user interest is critical for accurate news recommendation. Existing news recommendation methods usually infer user interest from click behaviors on news. However, users may click a news article because attracted by its title shown on the news website homepage, but may not be satisfied with its content after reading. In many cases users close the news page quickly after click. In this paper we propose to model user interest from both click behaviors on news titles and reading behaviors on news content for news recommendation. More specifically, we propose a personalized reading speed metric to measure users’ satisfaction with news content. We learn embeddings of users from the news content they have read and their satisfaction with these news to model their interest in news content. In addition, we also learn another user embedding from the news titles they have clicked to model their preference in news titles. We combine both kinds of user embeddings into a unified user representation for news recommendation. We train the user representation model using two supervised learning tasks built from user behaviors, i.e., news title based click prediction and news content based satisfaction prediction, to encourage our model to recommend the news articles which not only are likely to be clicked but also have the content satisfied by the user. Experiments on real-world dataset show our method can effectively boost the performance of user modeling for news recommendation.

52. Discovering Subsequence Patterns for Next POI Recommendation [PDF] 返回目录
IJCAI 2020.
Kangzhi Zhao, Yong Zhang, Hongzhi Yin, Jin Wang, Kai Zheng, Xiaofang Zhou, Chunxiao Xing
Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user's check-in sequence but ignore the subsequence patterns that often represent the socio-economic activities or coherence of preference of the users. However, it is challenging to integrate the semantic subsequences due to the difficulty to predefine the granularity of the complex but meaningful subsequences. In this paper, we propose Adaptive Sequence Partitioner with Power-law Attention (ASPPA) to automatically identify each semantic subsequence of POIs and discover their sequential patterns. Our model adopts a state-based stacked recurrent neural network to hierarchically learn the latent structures of the user's check-in sequence. We also design a power-law attention mechanism to integrate the domain knowledge in spatial and temporal contexts. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.

53. HyperNews: Simultaneous News Recommendation and Active-Time Prediction via a Double-Task Deep Neural Network [PDF] 返回目录
IJCAI 2020.
Rui Liu, Huilin Peng, Yong Chen, Dell Zhang
Personalized news recommendation can help users stay on top of the current affairs without being overwhelmed by the endless torrents of online news. However, the freshness or timeliness of news has been largely ignored by current news recommendation systems. In this paper, we propose a novel approach dubbed HyperNews which explicitly models the effect of timeliness on news recommendation. Furthermore, we introduce an auxiliary task of predicting the so-called "active-time" that users spend on each news article. Our key finding is that it is beneficial to address the problem of news recommendation together with the related problem of active-time prediction in a multi-task learning framework. Specifically, we train a double-task deep neural network (with a built-in timeliness module) to carry out news recommendation and active-time prediction simultaneously. To the best of our knowledge, such a "kill-two-birds-with-one-stone" solution has seldom been tried in the field of news recommendation before. Our extensive experiments on real-life news datasets have not only confirmed the mutual reinforcement of news recommendation and active-time prediction but also demonstrated significant performance improvements over state-of-the-art news recommendation techniques.

54. Learning the Compositional Visual Coherence for Complementary Recommendations [PDF] 返回目录
IJCAI 2020.
Zhi Li, Bo Wu, Qi Liu, Likang Wu, Hongke Zhao, Tao Mei
Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a Global Coherence Learning (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a Focal Coherence Learning (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods.

55. An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-ins [PDF] 返回目录
IJCAI 2020.
Lu Zhang, Zhu Sun, Jie Zhang, Yu Lei, Chen Li, Ziqing Wu, Horst Kloeden, Felix Klanner
Studies on next point-of-interest (POI) recommendation mainly seek to learn users' transition patterns with certain historical check-ins. However, in reality, users' movements are typically uncertain (i.e., fuzzy and incomplete) where most existing methods suffer from the transition pattern vanishing issue. To ease this issue, we propose a novel interactive multi-task learning (iMTL) framework to better exploit the interplay between activity and location preference. Specifically, iMTL introduces: (1) temporal-aware activity encoder equipped with fuzzy characterization over uncertain check-ins to unveil the latent activity transition patterns; (2) spatial-aware location preference encoder to capture the latent location transition patterns; and (3) task-specific decoder to make use of the learned latent transition patterns and enhance both activity and location prediction tasks in an interactive manner. Extensive experiments on three real-world datasets show the superiority of iMTL.

56. TopicKA: Generating Commonsense Knowledge-Aware Dialogue Responses Towards the Recommended Topic Fact [PDF] 返回目录
IJCAI 2020.
Sixing Wu, Ying Li, Dawei Zhang, Yang Zhou, Zhonghai Wu
Insufficient semantic understanding of dialogue always leads to the appearance of generic responses, in generative dialogue systems. Recently, high-quality knowledge bases have been introduced to enhance dialogue understanding, as well as to reduce the prevalence of boring responses. Although such knowledge-aware approaches have shown tremendous potential, they always utilize the knowledge in a black-box fashion. As a result, the generation process is somewhat uncontrollable, and it is also not interpretable. In this paper, we introduce a topic fact-based commonsense knowledge-aware approach, TopicKA. Different from previous works, TopicKA generates responses conditioned not only on the query message but also on a topic fact with an explicit semantic meaning, which also controls the direction of generation. Topic facts are recommended by a recommendation network trained under the Teacher-Student framework. To integrate the recommendation network and the generation network, this paper designs four schemes, which include two non-sampling schemes and two sampling methods. We collected and constructed a large-scale Chinese commonsense knowledge graph. Experimental results on an open Chinese benchmark dataset indicate that our model outperforms baselines in terms of both the objective and the subjective metrics.

57. Methodological Issues in Recommender Systems Research (Extended Abstract) [PDF] 返回目录
IJCAI 2020.
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
The development of continuously improved machine learning algorithms for personalized item ranking lies at the core of today's research in the area of recommender systems.Over the years, the research community has developed widely-agreed best practices for comparing algorithms and demonstrating progress with offline experiments. Unfortunately, we find this accepted research practice can easily lead to phantom progress due to the following reasons: limited reproducibility, comparison with complex but weak and non-optimized baseline algorithms, over-generalization from a small set of experimental configurations. To assess the extent of such problems, we analyzed 18 research papers published recently at top-ranked conferences. Only 7 were reproducible with reasonable effort, and 6 of them could often be outperformed by relatively simple heuristic methods, e.g., nearest neighbors. In this paper, we discuss these observations in detail, and reflect on the related fundamental problem of over-reliance on offline experiments in recommender systems research.

58. Dynamic Explainable Recommendation Based on Neural Attentive Models [PDF] 返回目录
AAAI 2019. AAAI Technical Track: AI and the Web
Xu Chen, Yongfeng Zhang, Zheng Qin
Providing explanations in a recommender system is getting more and more attention in both industry and research communities. Most existing explainable recommender models regard user preferences as invariant to generate static explanations. However, in real scenarios, a user’s preference is always dynamic, and she may be interested in different product features at different states. The mismatching between the explanation and user preference may degrade costumers’ satisfaction, confidence and trust for the recommender system.

59. DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System [PDF] 返回目录
AAAI 2019. AAAI Technical Track: AI and the Web
Zhi-Hong Deng, Ling Huang, Chang-Dong Wang, Jian-Huang Lai, Philip S. Yu
In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it’s almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning-based CF methods try to map users and items into a common representation space. In this case, the higher similarity between a user and an item in that space implies they match better. Matching function learning-based CF methods try to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the limited expressiveness of dot product and the weakness in capturing low-rank relations respectively. To this end, we propose a general framework named DeepCF, short for Deep Collaborative Filtering, to combine the strengths of the two types of methods and overcome such flaws. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DeepCF framework.

60. Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems [PDF] 返回目录
AAAI 2019. AAAI Technical Track: AI and the Web
Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, Jin Li
As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for crossdomain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods.

61. Discrete Social Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: AI and the Web
Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven C. H. Hoi
Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, which imposes formidable challenges for efficient and accurate recommendations. A promising way is to simply binarize the latent features (obtained in the training phase) and then compute the relevance score through Hamming distance. However, such a two-stage hashing based learning procedure is not capable of preserving the original data geometry in the real-value space and may result in a severe quantization loss. To address these issues, this work proposes a novel discrete social recommendation (DSR) method which learns binary codes in a unified framework for users and items, considering social information. We further put the balanced and uncorrelated constraints on the objective to ensure the learned binary codes can be informative yet compact, and finally develop an efficient optimization algorithm to estimate the model parameters. Extensive experiments on three real-world datasets demonstrate that DSR runs nearly 5 times faster and consumes only with 1/37 of its real-value competitor’s memory usage at the cost of almost no loss in accuracy.

62. Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System [PDF] 返回目录
AAAI 2019. AAAI Technical Track: AI and the Web
Hong Wen, Jing Zhang, Quan Lin, Keping Yang, Pipei Huang
Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named “ClickThrough Rate Prediction” (CTR) and “Conversion Rate Prediction” (CVR) are included, where CVR module is a crucial factor that affects the final purchasing volume directly. However, it is indeed very challenging due to its sparseness nature. In this paper, we tackle this problem by proposing multiLevel Deep Cascade Trees (ldcTree), which is a novel decision tree ensemble approach. It leverages deep cascade structures by stacking Gradient Boosting Decision Trees (GBDT) to effectively learn feature representation. In addition, we propose to utilize the cross-entropy in each tree of the preceding GBDT as the input feature representation for next level GBDT, which has a clear explanation, i.e., a traversal from root to leaf nodes in the next level GBDT corresponds to the combination of certain traversals in the preceding GBDT. The deep cascade structure and the combination rule enable the proposed ldcTree to have a stronger distributed feature representation ability. Moreover, inspired by ensemble learning, we propose an Ensemble ldcTree (E-ldcTree) to encourage the model’s diversity and enhance the representation ability further. Finally, we propose an improved Feature learning method based on EldcTree (F-EldcTree) for taking adequate use of weak and strong correlation features identified by pretrained GBDT models. Experimental results on off-line data set and online deployment demonstrate the effectiveness of the proposed methods.

63. Session-Based Recommendation with Graph Neural Networks [PDF] 返回目录
AAAI 2019. AAAI Technical Track: AI and the Web
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.

64. Hierarchical Reinforcement Learning for Course Recommendation in MOOCs [PDF] 返回目录
AAAI 2019. AAAI Technical Track: AI and the Web
Jing Zhang, Bowen Hao, Bo Chen, Cuiping Li, Hong Chen, Jimeng Sun
The proliferation of massive open online courses (MOOCs) demands an effective way of personalized course recommendation. The recent attention-based recommendation models can distinguish the effects of different historical courses when recommending different target courses. However, when a user has interests in many different courses, the attention mechanism will perform poorly as the effects of the contributing courses are diluted by diverse historical courses. To address such a challenge, we propose a hierarchical reinforcement learning algorithm to revise the user profiles and tune the course recommendation model on the revised profiles.

65. Joint Representation Learning for Multi-Modal Transportation Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Applications
Hao Liu, Ting Li, Renjun Hu, Yanjie Fu, Jingjing Gu, Hui Xiong
Multi-modal transportation recommendation has a goal of recommending a travel plan which considers various transportation modes, such as walking, cycling, automobile, and public transit, and how to connect among these modes. The successful development of multi-modal transportation recommendation systems can help to satisfy the diversified needs of travelers and improve the efficiency of transport networks. However, existing transport recommender systems mainly focus on unimodal transport planning. To this end, in this paper, we propose a joint representation learning framework for multi-modal transportation recommendation based on a carefully-constructed multi-modal transportation graph. Specifically, we first extract a multi-modal transportation graph from large-scale map query data to describe the concurrency of users, Origin-Destination (OD) pairs, and transport modes. Then, we provide effective solutions for the optimization problem and develop an anchor embedding for transport modes to initialize the embeddings of transport modes. Moreover, we infer user relevance and OD pair relevance, and incorporate them to regularize the representation learning. Finally, we exploit the learned representations for online multimodal transportation recommendations. Indeed, our method has been deployed into one of the largest navigation Apps to serve hundreds of millions of users, and extensive experimental results with real-world map query data demonstrate the enhanced performance of the proposed method for multimodal transportation recommendations.

66. GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Applications
Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.

67. From Recommendation Systems to Facility Location Games [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Game Theory and Economic Paradigms
Omer Ben-Porat, Gregory Goren, Itay Rosenberg, Moshe Tennenholtz
Recommendation systems are extremely popular tools for matching users and contents. However, when content providers are strategic, the basic principle of matching users to the closest content, where both users and contents are modeled as points in some semantic space, may yield low social welfare. This is due to the fact that content providers are strategic and optimize their offered content to be recommended to as many users as possible. Motivated by modern applications, we propose the widely studied framework of facility location games to study recommendation systems with strategic content providers. Our conceptual contribution is the introduction of a mediator to facility location models, in the pursuit of better social welfare. We aim at designing mediators that a) induce a game with high social welfare in equilibrium, and b) intervene as little as possible. In service of the latter, we introduce the notion of intervention cost, which quantifies how much damage a mediator may cause to the social welfare when an off-equilibrium profile is adopted. As a case study in high-welfare low-intervention mediator design, we consider the one-dimensional segment as the user domain. We propose a mediator that implements the socially optimal strategy profile as the unique equilibrium profile, and show a tight bound on its intervention cost. Ultimately, we consider some extensions, and highlight open questions for the general agenda.

68. Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Haokun Chen, Xinyi Dai, Han Cai, Weinan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, Yong Yu
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods.

69. Explainable Recommendation through Attentive Multi-View Learning [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Jingyue Gao, Xiting Wang, Yasha Wang, Xing Xie
Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods. The basic idea is to build an initial network based on an explainable deep hierarchy (e.g., Microsoft Concept Graph) and improve the model accuracy by optimizing key variables in the hierarchy (e.g., node importance and relevance). To ensure accurate rating prediction, we propose an attentive multi-view learning framework. The framework enables us to handle sparse and noisy data by co-regularizing among different feature levels and combining predictions attentively. To mine readable explanations from the hierarchy, we formulate personalized explanation generation as a constrained tree node selection problem and propose a dynamic programming algorithm to solve it. Experimental results show that our model outperforms state-of-the-art methods in terms of both accuracy and explainability.

70. Interaction-Aware Factorization Machines for Recommender Systems [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Fuxing Hong, Dongbo Huang, Ge Chen
Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named Interaction-aware Factorization Machine (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the feature aspect and the field aspect, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods.

71. HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Liang Hu, Songlei Jian, Longbing Cao, Zhiping Gu, Qingkui Chen, Artak Amirbekyan
Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.

72. From Zero-Shot Learning to Cold-Start Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang, Zi Huang
Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR. Specifically, we propose a Lowrank Linear Auto-Encoder (LLAE), which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper. LLAE consists of two parts, a low-rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user attributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win-win formulation, i.e., not only can CSR be handled by ZSL models with a significant performance improvement compared with several conventional state-of-the-art methods, but the consideration of CSR can benefit ZSL as well.

73. Non-Compensatory Psychological Models for Recommender Systems [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Chen Lin, Xiaolin Shen, Si Chen, Muhua Zhu, Yanghua Xiao
The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i.e. they evaluate an item over multiple aspects and compute a weighted or/and summated score which is used to derive the rating or ranking of the item. However, it has been shown in the literature of consumer behavior that, consumers adopt non-compensatory rules more often than compensatory rules. Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models.

74. RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, Maarten de Rijke
Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. repeat consumption is a common phenomenon in many recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), where the same item is re-consumed repeatedly over time. However, no previous studies have emphasized repeat consumption with neural networks. An effective neural approach is needed to decide when to perform repeat recommendation. In this paper, we incorporate a repeat-explore mechanism into neural networks and propose a new model, called RepeatNet, with an encoder-decoder structure. RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user’s history and recommends them at the right time. We report on extensive experiments on three benchmark datasets. RepeatNet outperforms state-of-the-art baselines on all three datasets in terms of MRR and Recall. Furthermore, as the dataset size and the repeat ratio increase, the improvements of RepeatNet over the baselines also increase, which demonstrates its advantage in handling repeat recommendation scenarios.

75. Evaluating Recommender System Stability with Influence-Guided Fuzzing [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
David Shriver, Sebastian G. Elbaum, Matthew B. Dwyer, David S. Rosenblum
Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender’s stability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset. We find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches.

76. Hierarchical Context Enabled Recurrent Neural Network for Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Kyungwoo Song, Mingi Ji, Sungrae Park, Il-Chul Moon
A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our interest drift assumption. As we suggest a new RNN structure, we support HCRNN with a complementary bi-channel attention structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.

77. An Integral Tag Recommendation Model for Textual Content [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Shijie Tang, Yuan Yao, Suwei Zhang, Feng Xu, Tianxiao Gu, Hanghang Tong, Xiaohui Yan, Jian Lu
Recommending suitable tags for online textual content is a key building block for better content organization and consumption. In this paper, we identify three pillars that impact the accuracy of tag recommendation: (1) sequential text modeling meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s) , (2) tag correlation meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) content-tag overlapping meaning that the vocabularies of content and tags are overlapped. However, none of the existing methods consider all these three aspects, leading to a suboptimal tag recommendation. In this paper, we propose an integral model to encode all the three aspects in a coherent encoder-decoder framework. In particular, (1) the encoder models the semantics of the textual content via Recurrent Neural Networks with the attention mechanism, (2) the decoder tackles the tag correlation with a prediction path, and (3) a shared embedding layer and an indicator function across encoder-decoder address the content-tag overlapping. Experimental results on three realworld datasets demonstrate that the proposed method significantly outperforms the existing methods in terms of recommendation accuracy.

78. Holographic Factorization Machines for Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Yi Tay, Shuai Zhang, Anh Tuan Luu, Siu Cheung Hui, Lina Yao, Tran Dang Quang Vinh
Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks. FMs are characterized by its usage of the inner product of factorized parameters to model pairwise feature interactions, making it highly expressive and powerful. This paper proposes Holographic Factorization Machines (HFM), a new novel method of enhancing the representation capability of FMs without increasing its parameter size. Our approach replaces the inner product in FMs with holographic reduced representations (HRRs), which are theoretically motivated by associative retrieval and compressed outer products. Empirically, we found that this leads to consistent improvements over vanilla FMs by up to 4% improvement in terms of mean squared error, with improvements larger at smaller parameterization. Additionally, we propose a neural adaptation of HFM which enhances its capability to handle nonlinear structures. We conduct extensive experiments on nine publicly available datasets for collaborative filtering with explicit feedback. HFM achieves state-of-theart performance on all nine, outperforming strong competitors such as Attentional Factorization Machines (AFM) and Neural Matrix Factorization (NeuMF).

79. CAMO: A Collaborative Ranking Method for Content Based Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Chengwei Wang, Tengfei Zhou, Chen Chen, Tianlei Hu, Gang Chen
In real-world recommendation tasks, feedback data are usually sparse. Therefore, a recommender’s performance is often determined by how much information that it can extract from textual contents. However, current methods do not make full use of the semantic information. They encode the textual contents either by “bag-of-words” technique or Recurrent Neural Network (RNN). The former neglects the order of words while the latter ignores the fact that textual contents can contain multiple topics. Besides, there exists a dilemma in designing a recommender. On the one hand, we shall use a sophisticated model to exploit every drop of information in item contents; on the other hand, we shall adopt a simple model to prevent itself from over-fitting when facing the sparse feedbacks. To fill the gaps, we propose a recommender named CAMO 1. CAMO employs a multi-layer content encoder for simultaneously capturing the semantic information of multitopic and word order. Moreover, CAMO makes use of adversarial training to prevent the complex encoder from overfitting. Extensive empirical studies show that CAMO outperforms state-of-the-art methods in predicting users’ preferences.

80. Explainable Reasoning over Knowledge Graphs for Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua
Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path.

81. Multi-Order Attentive Ranking Model for Sequential Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Lu Yu, Chuxu Zhang, Shangsong Liang, Xiangliang Zhang
In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interaction that can stand out the transition order between a pair of items, or union-level relation between a set of items and single one. However, most of existing work only captures one of them from a single view, especially on modeling the individual-level interaction. In this paper, we propose a Multi-order Attentive Ranking Model (MARank) to unify both individual- and union-level item interaction into preference inference model from multiple views. The idea is to represent user’s short-term preference by embedding user himself and a set of present items into multi-order features from intermedia hidden status of a deep neural network. With the help of attention mechanism, we can obtain a unified embedding to keep the individual-level interactions with a linear combination of mapped items’ features. Then, we feed the aggregated embedding to a designed residual neural network to capture union-level interaction. Thorough experiments are conducted to show the features of MARank under various component settings. Furthermore experimental results on several public datasets show that MARank significantly outperforms the state-of-the-art baselines on different evaluation metrics. The source code can be found at https://github.com/voladorlu/MARank.

82. Hashtag Recommendation for Photo Sharing Services [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Suwei Zhang, Yuan Yao, Feng Xu, Hanghang Tong, Xiaohui Yan, Jian Lu
Hashtags can greatly facilitate content navigation and improve user engagement in social media. Meaningful as it might be, recommending hashtags for photo sharing services such as Instagram and Pinterest remains a daunting task due to the following two reasons. On the endogenous side, posts in photo sharing services often contain both images and text, which are likely to be correlated with each other. Therefore, it is crucial to coherently model both image and text as well as the interaction between them. On the exogenous side, hashtags are generated by users and different users might come up with different tags for similar posts, due to their different preference and/or community effect. Therefore, it is highly desirable to characterize the users’ tagging habits. In this paper, we propose an integral and effective hashtag recommendation approach for photo sharing services. In particular, the proposed approach considers both the endogenous and exogenous effects by a content modeling module and a habit modeling module, respectively. For the content modeling module, we adopt the parallel co-attention mechanism to coherently model both image and text as well as the interaction between them; for the habit modeling module, we introduce an external memory unit to characterize the historical tagging habit of each user. The overall hashtag recommendations are generated on the basis of both the post features from the content modeling module and the habit influences from the habit modeling module. We evaluate the proposed approach on real Instagram data. The experimental results demonstrate that the proposed approach significantly outperforms the state-of-theart methods in terms of recommendation accuracy, and that both content modeling and habit modeling contribute significantly to the overall recommendation accuracy.

83. Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou
Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.

84. DAN: Deep Attention Neural Network for News Recommendation [PDF] 返回目录
AAAI 2019. AAAI Technical Track: Machine Learning
Qiannan Zhu, Xiaofei Zhou, Zeliang Song, Jianlong Tan, Li Guo
With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail with the dynamic diversity of news and user’s interests, or ignore the importance of sequential information of user’s clicking selection. In this paper, taking full advantages of convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation. Our DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention-based RNN for capturing richer hidden sequential features of user’s clicks, and combines these features for new recommendation. We conduct experiment on real-world news data sets, and the experimental results demonstrate the superiority and effectiveness of our proposed DAN model.

85. Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation [PDF] 返回目录
AAAI 2019. IAAI Technical Track: Emerging Papers
Stéphane Martin, Boi Faltings, Vincent Schickel
We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SSAGD, is a novel variant of the Matrix-Factorization technique (MF), which is considered state-of-the-art in the recommender literature.

86. Recommender Systems: A Healthy Obsession [PDF] 返回目录
AAAI 2019. Senior Member Presentation Track: Blue Sky Papers
Barry Smyth
We propose endurance sports as a rich and novel domain for recommender systems and machine learning research. As sports like marathon running, triathlons, and mountain biking become more and more popular among recreational athletes, there exists a growing opportunity to develop solutions to a number of interesting prediction, classification, and recommendation challenges, to better support the complex training and competition needs of athletes. Such solutions have the potential to improve the health and well-being of large populations of users, by promoting and optimising exercise as part of a productive and healthy lifestyle.

87. Cross-Domain Recommendation via Coupled Factorization Machines [PDF] 返回目录
AAAI 2019. Student Abstract Track
Lile Li, Quan Do, Wei Liu
Data across many business domains can be represented by two or more coupled data sets. Correlations among these coupled datasets have been studied in the literature for making more accurate cross-domain recommender systems. However, in existing methods, cross-domain recommendations mostly assume the coupled mode of data sets share identical latent factors, which limits the discovery of potentially useful domain-specific properties of the original data. In this paper, we proposed a novel cross-domain recommendation method called Coupled Factorization Machine (CoFM) that addresses this limitation. Compared to existing models, our research is the first model that uses factorization machines to capture both common characteristics of coupled domains while simultaneously preserving the differences among them. Our experiments with real-world datasets confirm the advantages of our method in making across-domain recommendations.

88. Neural News Recommendation with Long- and Short-term User Representations [PDF] 返回目录
ACL 2019.
Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie
Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both long-term preferences and short-term interests. However, existing news recommendation methods usually learn single representations of users, which may be insufficient. In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations. The core of our approach is a news encoder and a user encoder. In the news encoder, we learn representations of news from their titles and topic categories, and use attention network to select important words. In the user encoder, we propose to learn long-term user representations from the embeddings of their IDs.In addition, we propose to learn short-term user representations from their recently browsed news via GRU network. Besides, we propose two methods to combine long-term and short-term user representations. The first one is using the long-term user representation to initialize the hidden state of the GRU network in short-term user representation. The second one is concatenating both long- and short-term user representations as a unified user vector. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of neural news recommendation.

89. DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions [PDF] 返回目录
ACL 2019.
Tirthankar Ghosal, Rajeev Verma, Asif Ekbal, Pushpak Bhattacharyya
Automatically validating a research artefact is one of the frontiers in Artificial Intelligence (AI) that directly brings it close to competing with human intellect and intuition. Although criticised sometimes, the existing peer review system still stands as the benchmark of research validation. The present-day peer review process is not straightforward and demands profound domain knowledge, expertise, and intelligence of human reviewer(s), which is somewhat elusive with the current state of AI. However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration. Here in this work, we investigate the role of reviewer sentiment embedded within peer review texts to predict the peer review outcome. Our proposed deep neural architecture takes into account three channels of information: the paper, the corresponding reviews, and review’s polarity to predict the overall recommendation score as well as the final decision. We achieve significant performance improvement over the baselines (∼ 29% error reduction) proposed in a recently released dataset of peer reviews. An AI of this kind could assist the editors/program chairs as an additional layer of confidence, especially when non-responding/missing reviewers are frequent in present day peer review.

90. Neural News Recommendation with Topic-Aware News Representation [PDF] 返回目录
ACL 2019.
Chuhan Wu, Fangzhao Wu, Mingxiao An, Yongfeng Huang, Xing Xie
News recommendation can help users find interested news and alleviate information overload. The topic information of news is critical for learning accurate news and user representations for news recommendation. However, it is not considered in many existing news recommendation methods. In this paper, we propose a neural news recommendation approach with topic-aware news representations. The core of our approach is a topic-aware news encoder and a user encoder. In the news encoder we learn representations of news from their titles via CNN networks and apply attention networks to select important words. In addition, we propose to learn topic-aware news representations by jointly training the news encoder with an auxiliary topic classification task. In the user encoder we learn the representations of users from their browsed news and use attention networks to select informative news for user representation learning. Extensive experiments on a real-world dataset validate the effectiveness of our approach.

91. Neural-based Chinese Idiom Recommendation for Enhancing Elegance in Essay Writing [PDF] 返回目录
ACL 2019.
Yuanchao Liu, Bo Pang, Bingquan Liu
Although the proper use of idioms can enhance the elegance of writing, the active use of various expressions is a challenge because remembering idioms is difficult. In this study, we address the problem of idiom recommendation by leveraging a neural machine translation framework, in which we suppose that idioms are written with one pseudo target language. Two types of real-life datasets are collected to support this study. Experimental results show that the proposed approach achieves promising performance compared with other baseline methods.

92. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects [PDF] 返回目录
EMNLP 2019.
Jianmo Ni, Jiacheng Li, Julian McAuley
Several recent works have considered the problem of generating reviews (or ‘tips’) as a form of explanation as to why a recommendation might match a customer’s interests. While promising, we demonstrate that existing approaches struggle (in terms of both quality and content) to generate justifications that are relevant to users’ decision-making process. We seek to introduce new datasets and methods to address the recommendation justification task. In terms of data, we first propose an ‘extractive’ approach to identify review segments which justify users’ intentions; this approach is then used to distantly label massive review corpora and construct large-scale personalized recommendation justification datasets. In terms of generation, we are able to design two personalized generation models with this data: (1) a reference-based Seq2Seq model with aspect-planning which can generate justifications covering different aspects, and (2) an aspect-conditional masked language model which can generate diverse justifications based on templates extracted from justification histories. We conduct experiments on two real-world datasets which show that our model is capable of generating convincing and diverse justifications.

93. Towards Knowledge-Based Recommender Dialog System [PDF] 返回目录
EMNLP 2019.
Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang
In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System. It integrates the recommender system and the dialog generation system. The dialog generation system can enhance the performance of the recommendation system by introducing information about users’ preferences, and the recommender system can improve that of the dialog generation system by providing recommendation-aware vocabulary bias. Experimental results demonstrate that our proposed model has significant advantages over the baselines in both the evaluation of dialog generation and recommendation. A series of analyses show that the two systems can bring mutual benefits to each other, and the introduced knowledge contributes to both their performances.

94. Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue [PDF] 返回目录
EMNLP 2019.
Dongyeop Kang, Anusha Balakrishnan, Pararth Shah, Paul Crook, Y-Lan Boureau, Jason Weston
Traditional recommendation systems produce static rather than interactive recommendations invariant to a user’s specific requests, clarifications, or current mood, and can suffer from the cold-start problem if their tastes are unknown. These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone’s preferences, react to their requests, and recommend more appropriate items. In this work, we collect a goal-driven recommendation dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260 conversation turns between pairs of human workers recommending movies to each other. The task is specifically designed as a cooperative game between two players working towards a quantifiable common goal. We leverage the dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. Models are first trained to imitate the behavior of human players without considering the task goal itself (supervised training). We then finetune our models on simulated bot-bot conversations between two paired pre-trained models (bot-play), in order to achieve the dialogue goal. Our experiments show that models finetuned with bot-play learn improved dialogue strategies, reach the dialogue goal more often when paired with a human, and are rated as more consistent by humans compared to models trained without bot-play. The dataset and code are publicly available through the ParlAI framework.

95. Neural Conversation Recommendation with Online Interaction Modeling [PDF] 返回目录
EMNLP 2019.
Xingshan Zeng, Jing Li, Lu Wang, Kam-Fai Wong
The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis. It presents a concrete challenge for individuals to better discover and engage in social media discussions. In this paper, we present a novel framework to automatically recommend conversations to users based on their prior conversation behaviors. Built on neural collaborative filtering, our model explores deep semantic features that measure how a user’s preferences match an ongoing conversation’s context. Furthermore, to identify salient characteristics from interleaving user interactions, our model incorporates graph-structured networks, where both replying relations and temporal features are encoded as conversation context. Experimental results on two large-scale datasets collected from Twitter and Reddit show that our model yields better performance than previous state-of-the-art models, which only utilize lexical features and ignore past user interactions in the conversations.

96. Neural News Recommendation with Heterogeneous User Behavior [PDF] 返回目录
EMNLP 2019.
Chuhan Wu, Fangzhao Wu, Mingxiao An, Tao Qi, Jianqiang Huang, Yongfeng Huang, Xing Xie
News recommendation is important for online news platforms to help users find interested news and alleviate information overload. Existing news recommendation methods usually rely on the news click history to model user interest. However, these methods may suffer from the data sparsity problem, since the news click behaviors of many users in online news platforms are usually very limited. Fortunately, some other kinds of user behaviors such as webpage browsing and search queries can also provide useful clues of users’ news reading interest. In this paper, we propose a neural news recommendation approach which can exploit heterogeneous user behaviors. Our approach contains two major modules, i.e., news representation and user representation. In the news representation module, we learn representations of news from their titles via CNN networks, and apply attention networks to select important words. In the user representation module, we propose an attentive multi-view learning framework to learn unified representations of users from their heterogeneous behaviors such as search queries, clicked news and browsed webpages. In addition, we use word- and record-level attentions to select informative words and behavior records. Experiments on a real-world dataset validate the effectiveness of our approach.

97. Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network [PDF] 返回目录
EMNLP 2019.
Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie
User and item representation learning is critical for recommendation. Many of existing recommendation methods learn representations of users and items based on their ratings and reviews. However, the user-user and item-item relatedness are usually not considered in these methods, which may be insufficient. In this paper, we propose a neural recommendation approach which can utilize useful information from both review content and user-item graphs. Since reviews and graphs have different characteristics, we propose to use a multi-view learning framework to incorporate them as different views. In the review content-view, we propose to use a hierarchical model to first learn sentence representations from words, then learn review representations from sentences, and finally learn user/item representations from reviews. In addition, we propose to incorporate a three-level attention network into this view to select important words, sentences and reviews for learning informative user and item representations. In the graph-view, we propose a hierarchical graph neural network to jointly model the user-item, user-user and item-item relatedness by capturing the first- and second-order interactions between users and items in the user-item graph. In addition, we apply attention mechanism to model the importance of these interactions to learn informative user and item representations. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.

98. Neural News Recommendation with Multi-Head Self-Attention [PDF] 返回目录
EMNLP 2019.
Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, Xing Xie
News recommendation can help users find interested news and alleviate information overload. Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations. In this paper, we propose a neural news recommendation approach with multi-head self-attention (NRMS). The core of our approach is a news encoder and a user encoder. In the news encoder, we use multi-head self-attentions to learn news representations from news titles by modeling the interactions between words. In the user encoder, we learn representations of users from their browsed news and use multi-head self-attention to capture the relatedness between the news. Besides, we apply additive attention to learn more informative news and user representations by selecting important words and news. Experiments on a real-world dataset validate the effectiveness and efficiency of our approach.

99. Generative Adversarial User Model for Reinforcement Learning Based Recommendation System [PDF] 返回目录
ICML 2019.
Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither the reward function nor the environment dynamics are clearly defined, making the application of RL challenging. In this paper, we propose a novel model-based reinforcement learning framework for recommendation systems, where we develop a generative adversarial network to imitate user behavior dynamics and learn her reward function. Using this user model as the simulation environment, we develop a novel Cascading DQN algorithm to obtain a combinatorial recommendation policy which can handle a large number of candidate items efficiently. In our experiments with real data, we show this generative adversarial user model can better explain user behavior than alternatives, and the RL policy based on this model can lead to a better long-term reward for the user and higher click rate for the system.

100. Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems [PDF] 返回目录
ICML 2019.
Timothy A. Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan
Predicting delayed outcomes is an important problem in recommender systems (e.g., if customers will finish reading an ebook). We formalize the problem as an adversarial, delayed online learning problem and consider how a proxy for the delayed outcome (e.g., if customers read a third of the book in 24 hours) can help minimize regret, even though the proxy is not available when making a prediction. Motivated by our regret analysis, we propose two neural network architectures: Factored Forecaster (FF) which is ideal if the proxy is informative of the outcome in hindsight, and Residual Factored Forecaster (RFF) that is robust to a non-informative proxy. Experiments on two real-world datasets for predicting human behavior show that RFF outperforms both FF and a direct forecaster that does not make use of the proxy. Our results suggest that exploiting proxies by factorization is a promising way to mitigate the impact of long delays in human-behavior prediction tasks.

101. Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random [PDF] 返回目录
ICML 2019.
Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi
In recommender systems, usually the ratings of a user to most items are missing and a critical problem is that the missing ratings are often missing not at random (MNAR) in reality. It is widely acknowledged that MNAR ratings make it difficult to accurately predict the ratings and unbiasedly estimate the performance of rating prediction. Recent approaches use imputed errors to recover the prediction errors for missing ratings, or weight observed ratings with the propensities of being observed. These approaches can still be severely biased in performance estimation or suffer from the variance of the propensities. To overcome these limitations, we first propose an estimator that integrates the imputed errors and propensities in a doubly robust way to obtain unbiased performance estimation and alleviate the effect of the propensity variance. To achieve good performance guarantees, based on this estimator, we propose joint learning of rating prediction and error imputation, which outperforms the state-of-the-art approaches on four real-world datasets.

102. Deep Adversarial Social Recommendation [PDF] 返回目录
IJCAI 2019.
Wenqi Fan, Tyler Derr, Yao Ma, Jianping Wang, Jiliang Tang, Qing Li
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life. The majority of existing social recommendation methods unify user representation for the user-item interactions (item domain) and user-user connections (social domain). However, it may restrain user representation learning in each respective domain, since users behave and interact differently in the two domains, which makes their representations to be heterogeneous. In addition, most of traditional recommender systems can not efficiently optimize these objectives, since they utilize negative sampling technique which is unable to provide enough informative guidance towards the training during the optimization process. In this paper, to address the aforementioned challenges, we propose a novel deep adversarial social recommendation framework DASO. It adopts a bidirectional mapping method to transfer users' information between social domain and item domain using adversarial learning. Comprehensive experiments on two real-world datasets show the effectiveness of the proposed framework.

103. Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation [PDF] 返回目录
IJCAI 2019.
Guibing Guo, Shichang Ouyang, Xiaodong He, Fajie Yuan, Xiaohua Liu
Sequential recommendation systems have become a research hotpot recently to suggest users with the next item of interest (to interact with). However, existing approaches suffer from two limitations: (1) The representation of an item is relatively static and fixed for all users. We argue that even a same item should be represented distinctively with respect to different users and time steps. (2) The generation of a prediction for a user over an item is computed in a single scale (e.g., by their inner product), ignoring the nature of multi-scale user preferences. To resolve these issues, in this paper we propose two enhancing building blocks for sequential recommendation. Specifically, we devise a Dynamic Item Block (DIB) to learn dynamic item representation by aggregating the embeddings of those who rated the same item before that time step. Then, we come up with a Prediction Enhancing Block (PEB) to project user representation into multiple scales, based on which many predictions can be made and attentively aggregated for enhanced learning. Each prediction is generated by a softmax over a sampled itemset rather than the whole item space for efficiency. We conduct a series of experiments on four real datasets, and show that even a basic model can be greatly enhanced with the involvement of DIB and PEB in terms of ranking accuracy. The code and datasets can be obtained from https://github.com/ouououououou/DIB-PEB-Sequential-RS

104. Discrete Trust-aware Matrix Factorization for Fast Recommendation [PDF] 返回目录
IJCAI 2019.
Guibing Guo, Enneng Yang, Li Shen, Xiaochun Yang, Xiaodong He
Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

105. Minimizing Time-to-Rank: A Learning and Recommendation Approach [PDF] 返回目录
IJCAI 2019.
Haoming Li, Sujoy Sikdar, Rohit Vaish, Junming Wang, Lirong Xia, Chaonan Ye
Consider the following problem faced by an online voting platform: A user is provided with a list of alternatives, and is asked to rank them in order of preference using only drag-and-drop operations. The platform's goal is to recommend an initial ranking that minimizes the time spent by the user in arriving at her desired ranking. We develop the first optimization framework to address this problem, and make theoretical as well as practical contributions. On the practical side, our experiments on the Amazon Mechanical Turk platform provide two interesting insights about user behavior: First, that users' ranking strategies closely resemble selection or insertion sort, and second, that the time taken for a drag-and-drop operation depends linearly on the number of positions moved. These insights directly motivate our theoretical model of the optimization problem. We show that computing an optimal recommendation is NP-hard, and provide exact and approximation algorithms for a variety of special cases of the problem. Experimental evaluation on MTurk shows that, compared to a random recommendation strategy, the proposed approach reduces the (average) time-to-rank by up to 50%.

106. Personalized Multimedia Item and Key Frame Recommendation [PDF] 返回目录
IJCAI 2019.
Le Wu, Lei Chen, Yonghui Yang, Richang Hong, Yong Ge, Xing Xie, Meng Wang
When recommending or advertising items to users, an emerging trend is to present each multimedia item with a key frame image (e.g., the poster of a movie). As each multimedia item can be represented as multiple fine-grained visual images (e.g., related images of the movie), personalized key frame recommendation is necessary in these applications to attract users' unique visual preferences. However, previous personalized key frame recommendation models relied on users' fine grained image behavior of multimedia items (e.g., user-image interaction behavior), which is often not available in real scenarios. In this paper, we study the general problem of joint multimedia item and key frame recommendation in the absence of the fine-grained user-image behavior. We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior. To tackle this challenge, we leverage users' item behavior by projecting users(items) in two latent spaces: a collaborative latent space and a visual latent space. We further design a model to discern both the collaborative and visual dimensions of users, and model how users make decisive item preferences from these two spaces. As a result, the learned user visual profiles could be directly applied for key frame recommendation. Finally, experimental results on a real-world dataset clearly show the effectiveness of our proposed model on the two recommendation tasks.

107. DeepAPF: Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation [PDF] 返回目录
IJCAI 2019.
Huan Yan, Xiangning Chen, Chen Gao, Yong Li, Depeng Jin
Existing web video systems recommend videos according to users' viewing history from its own website. However, since many users watch videos in multiple websites, this approach fails to capture these users' interests across sites. In this paper, we investigate the user viewing behavior in multiple sites based on a large scale real dataset. We find that user interests are comprised of cross-site consistent part and site-specific part with different degrees of the importance. Existing linear matrix factorization recommendation model has limitation in modeling such complicated interactions. Thus, we propose a model of Deep Attentive Probabilistic Factorization (DeepAPF) to exploit deep learning method to approximate such complex user-video interaction. DeepAPF captures both cross-site common interests and site-specific interests with non-uniform importance weights learned by the attentional network. Extensive experiments show that our proposed model outperforms by 17.62%, 7.9% and 8.1% with the comparison of three state-of-the-art baselines. Our study provides insight to integrate user viewing records from multiple sites via the trusted third party, which gains mutual benefits in video recommendation.

108. Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism [PDF] 返回目录
IJCAI 2019.
Wei Liu, Zhi-Jie Wang, Bin Yao, Jian Yin
Learning user’s preference from check-in data isimportant for POI recommendation. Yet, a userusually has visited some POIs while most of POIsare unvisited (i.e., negative samples). To leveragethese “no-behavior” POIs, a typical approachis pairwise ranking, which constructs ranking pairsfor the user and POIs. Although this approach isgenerally effective, the negative samples in rankingpairs are obtained randomly, which may fail toleverage “critical” negative samples in the modeltraining. On the other hand, previous studies alsoutilized geographical feature to improve the recommendationquality. Nevertheless, most of previousworks did not exploit geographical informationcomprehensively, which may also affect the performance.To alleviate these issues, we propose a geographicalinformation based adversarial learningmodel (Geo-ALM), which can be viewed as a fusionof geographic features and generative adversarialnetworks. Its core idea is to learn the discriminatorand generator interactively, by exploiting twogranularity of geographic features (i.e., region andPOI features). Experimental results show that Geo-ALM can achieve competitive performance, comparedto several state-of-the-arts.

109. Multi-View Active Learning for Video Recommendation [PDF] 返回目录
IJCAI 2019.
Jia-Jia Cai, Jun Tang, Qing-Guo Chen, Yao Hu, Xiaobo Wang, Sheng-Jun Huang
On many video websites, the recommendation is implemented as a prediction problem of video-user pairs, where the videos are represented by text features extracted from the metadata. However, the metadata is manually annotated by users and is usually missing for online videos. To train an effective recommender system with lower annotation cost, we propose an active learning approach to fully exploit the visual view of videos, while querying as few annotations as possible from the text view. On one hand, a joint model is proposed to learn the mapping from visual view to text view by simultaneously aligning the two views and minimizing the classification loss. On the other hand, a novel strategy based on prediction inconsistency and watching frequency is proposed to actively select the most important videos for metadata querying. Experiments on both classification datasets and real video recommendation tasks validate that the proposed approach can significantly reduce the annotation cost.

110. Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network [PDF] 返回目录
IJCAI 2019.
Liang Chen, Yang Liu, Xiangnan He, Lianli Gao, Zibin Zheng
Most recommendation research has been concentrated on recommending single items to users, such as the considerable work on collaborative filtering that models the interaction between a user and an item. However, in many real-world scenarios, the platform needs to show users a set of items, e.g., the marketing strategy that offers multiple items for sale as one bundle.In this work, we consider recommending a set of items to a user, i.e., the Bundle Recommendation task, which concerns the interaction modeling between a user and a set of items. We contribute a neural network solution named DAM, short for Deep Attentive Multi-Task model, which is featured with two special designs: 1) We design a factorized attention network to aggregate the item embeddings in a bundle to obtain the bundle's representation; 2) We jointly model user-bundle interactions and user-item interactions in a multi-task manner to alleviate the scarcity of user-bundle interactions. Extensive experiments on a real-world dataset show that DAM outperforms the state-of-the-art solution, verifying the effectiveness of our attention design and multi-task learning in DAM.

111. Co-Attentive Multi-Task Learning for Explainable Recommendation [PDF] 返回目录
IJCAI 2019.
Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, Enhong Chen
Despite widespread adoption, recommender systems remain mostly black boxes. Recently, providing explanations about why items are recommended has attracted increasing attention due to its capability to enhance user trust and satisfaction. In this paper, we propose a co-attentive multi-task learning model for explainable recommendation. Our model improves both prediction accuracy and explainability of recommendation by fully exploiting the correlations between the recommendation task and the explanation task. In particular, we design an encoder-selector-decoder architecture inspired by human's information-processing model in cognitive psychology. We also propose a hierarchical co-attentive selector to effectively model the cross knowledge transferred for both tasks. Our model not only enhances prediction accuracy of the recommendation task, but also generates linguistic explanations that are fluent, useful, and highly personalized. Experiments on three public datasets demonstrate the effectiveness of our model.

112. Recommending Links to Maximize the Influence in Social Networks [PDF] 返回目录
IJCAI 2019.
Federico Corò, Gianlorenzo D'Angelo, Yllka Velaj
Social link recommendation systems, like "People-you-may-know" on Facebook, "Who-to-follow" on Twitter, and "Suggested-Accounts" on Instagram assist the users of a social network in establishing new connections with other users. While these systems are becoming more and more important in the growth of social media, they tend to increase the popularity of users that are already popular. Indeed, since link recommenders aim at predicting users' behavior, they accelerate the creation of links that are likely to be created in the future, and, as a consequence, they reinforce social biases by suggesting few (popular) users, while giving few chances to the majority of users to build new connections and increase their popularity.In this paper we measure the popularity of a user by means of its social influence, which is its capability to influence other users' opinions, and we propose a link recommendation algorithm that evaluates the links to suggest according to their increment in social influence instead of their likelihood of being created. In detail, we give a constant factor approximation algorithm for the problem of maximizing the social influence of a given set of target users by suggesting a fixed number of new connections. We experimentally show that, with few new links and small computational time, our algorithm is able to increase by far the social influence of the target users. We compare our algorithm with several baselines and show that it is the most effective one in terms of increased influence.

113. Reinforced Negative Sampling for Recommendation with Exposure Data [PDF] 返回目录
IJCAI 2019.
Jingtao Ding, Yuhan Quan, Xiangnan He, Yong Li, Depeng Jin
In implicit feedback-based recommender systems, user exposure data, which record whether or not a recommended item has been interacted by a user, provide an important clue on selecting negative training samples. In this work, we improve the negative sampler by integrating the exposure data. We propose to generate high-quality negative instances by adversarial training to favour the difﬁcult instances, and by optimizing additional objective to favour the real negatives in exposure data. However, this idea is non-trivial to implement since the distribution of exposure data is latent and the item space is discrete. To this end, we design a novel RNS method (short for Reinforced Negative Sampler) that generates exposure-alike negative instances through feature matching technique instead of directly choosing from exposure data. Optimized under the reinforcement learning framework, RNS is able to integrate user preference signals in exposure data and hard negatives. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our RNS method. Our implementation is available at: https://github. com/dingjingtao/ReinforceNS.

114. RecoNet: An Interpretable Neural Architecture for Recommender Systems [PDF] 返回目录
IJCAI 2019.
Francesco Fusco, Michalis Vlachos, Vasileios Vasileiadis, Kathrin Wardatzky, Johannes Schneider
Neural systems offer high predictive accuracy but are plagued by long training times and low interpretability. We present a simple neural architecture for recommender systems that lifts several of these shortcomings. Firstly, the approach has a high predictive power that is comparable to state-of-the-art recommender approaches. Secondly, owing to its simplicity, the trained model can be interpreted easily because it provides the individual contribution of each input feature to the decision. Our method is three orders of magnitude faster than general-purpose explanatory approaches, such as LIME. Finally, thanks to its design, our architecture addresses cold-start issues, and therefore the model does not require retraining in the presence of new users.

115. Hybrid Item-Item Recommendation via Semi-Parametric Embedding [PDF] 返回目录
IJCAI 2019.
Peng Hu, Rong Du, Yao Hu, Nan Li
Nowadays, item-item recommendation plays an important role in modern recommender systems. Traditionally, this is either solved by behavior-based collaborative filtering or content-based meth- ods. However, both kinds of methods often suffer from cold-start problems, or poor performance due to few behavior supervision; and hybrid methods which can leverage the strength of both kinds of methods are needed. In this paper, we propose a semi-parametric embedding framework for this problem. Specifically, the embedding of an item is composed of two parts, i.e., the parametric part from content information and the non-parametric part designed to encode behavior information; meanwhile, a deep learning algorithm is proposed to learn two parts simultaneously. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of the proposed method.

116. SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets [PDF] 返回目录
IJCAI 2019.
Eugene Ie, Vihan Jain, Jing Wang, Sanmit Narvekar, Ritesh Agarwal, Rui Wu, Heng-Tze Cheng, Tushar Chandra, Craig Boutilier
Reinforcement learning methods for recommender systems optimize recommendations for long-term user engagement. However, since users are often presented with slates of multiple items---which may have interacting effects on user choice---methods are required to deal with the combinatorics of the RL action space. We develop SlateQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates. Under mild assumptions on user choice behavior, we show that the long-term value (LTV) of a slate can be decomposed into a tractable function of its component item-wise LTVs. We demonstrate our methods in simulation, and validate the scalability and effectiveness of decomposed TD-learning on YouTube.

117. Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty [PDF] 返回目录
IJCAI 2019.
Junyang Jiang, Deqing Yang, Yanghua Xiao, Chenlu Shen
Most of existing embedding based recommendation models use embeddings (vectors) to represent users and items which contain latent features of users and items. Each of such embeddings corresponds to a single fixed point in low-dimensional space, thus fails to precisely represent the users/items with uncertainty which are often observed in recommender systems. Addressing this problem, we propose a unified deep recommendation framework employing Gaussian embeddings, which are proven adaptive to uncertain preferences exhibited by some users, resulting in better user representations and recommendation performance. Furthermore, our framework adopts Monte-Carlo sampling and convolutional neural networks to compute the correlation between the objective user and the candidate item, based on which precise recommendations are achieved. Our extensive experiments on two benchmark datasets not only justify that our proposed Gaussian embeddings capture the uncertainty of users very well, but also demonstrate its superior performance over the state-of-the-art recommendation models.

118. Sequential and Diverse Recommendation with Long Tail [PDF] 返回目录
IJCAI 2019.
Yejin Kim, Kwangseob Kim, Chanyoung Park, Hwanjo Yu
Sequential recommendation is a task that learns a temporal dynamic of a user behavior in sequential data and predicts items that a user would like afterward. However, diversity has been rarely emphasized in the context of sequential recommendation. Sequential and diverse recommendation must learn temporal preference on diverse items as well as on general items. Thus, we propose a sequential and diverse recommendation model that predicts a ranked list containing general items and also diverse items without compromising significant accuracy.To learn temporal preference on diverse items as well as on general items, we cluster and relocate consumed long tail items to make a pseudo ground truth for diverse items and learn the preference on long tail using recurrent neural network, which enables us to directly learn a ranking function. Extensive online and offline experiments deployed on a commercial platform demonstrate that our models significantly increase diversity while preserving accuracy compared to the state-of-the-art sequential recommendation model, and consequently our models improve user satisfaction.

119. Correlation-Sensitive Next-Basket Recommendation [PDF] 返回目录
IJCAI 2019.
Duc-Trong Le, Hady W. Lauw, Yuan Fang
Items adopted by a user over time are indicative of the underlying preferences. We are concerned with learning such preferences from observed sequences of adoptions for recommendation. As multiple items are commonly adopted concurrently, e.g., a basket of grocery items or a sitting of media consumption, we deal with a sequence of baskets as input, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of related items that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating information on pairwise correlations among items would help to arrive at more coherent basket recommendations. Towards this objective, we develop a hierarchical network architecture codenamed Beacon to model basket sequences. Each basket is encoded taking into account the relative importance of items and correlations among item pairs. This encoding is utilized to infer sequential associations along the basket sequence. Extensive experiments on three public real-life datasets showcase the effectiveness of our approach for the next-basket recommendation problem.

120. A Review-Driven Neural Model for Sequential Recommendation [PDF] 返回目录
IJCAI 2019.
Chenliang Li, Xichuan Niu, Xiangyang Luo, Zhenzhong Chen, Cong Quan
Writing review for a purchased item is a unique channel to express a user's opinion in E-Commerce. Recently, many deep learning based solutions have been proposed by exploiting user reviews for rating prediction. In contrast, there has been few attempt to enlist the semantic signals covered by user reviews for the task of collaborative filtering. In this paper, we propose a novel review-driven neural sequential recommendation model (named RNS) by considering user's intrinsic preference (long-term) and sequential patterns (short-term). In detail, RNS is devised to encode each user or item with the aspect-aware representations extracted from the reviews. Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level. Extensive experiments on three real-world datasets of different domains demonstrate that RNS obtains significant performance improvement over uptodate state-of-the-art sequential recommendation models.

121. DMRAN: A Hierarchical Fine-Grained Attention-Based Network for Recommendation [PDF] 返回目录
IJCAI 2019.
Huizhao Wang, Guanfeng Liu, An Liu, Zhixu Li, Kai Zheng
The conventional methods for the next-item recommendation are generally based on RNN or one- dimensional attention with time encoding. They are either hard to preserve the long-term dependencies between different interactions, or hard to capture fine-grained user preferences. In this paper, we propose a Double Most Relevant Attention Network (DMRAN) that contains two layers, i.e., Item level Attention and Feature Level Self- attention, which are to pick out the most relevant items from the sequence of user’s historical behaviors, and extract the most relevant aspects of relevant items, respectively. Then, we can capture the fine-grained user preferences to better support the next-item recommendation. Extensive experiments on two real-world datasets illustrate that DMRAN can improve the efficiency and effectiveness of the recommendation compared with the state-of-the-art methods.

122. Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks [PDF] 返回目录
IJCAI 2019.
Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao
A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subsethave strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. Filling gaps in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRN) with a purpose routing network to detect the purposes of each item and assign it into the corresponding channels. Moreover, a purpose specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity.

123. Unified Embedding Model over Heterogeneous Information Network for Personalized Recommendation [PDF] 返回目录
IJCAI 2019.
Zekai Wang, Hongzhi Liu, Yingpeng Du, Zhonghai Wu, Xing Zhang
Most of heterogeneous information network (HIN) based recommendation models are based on the user and item modeling with meta-paths. However, they always model users and items in isolation under each meta-path, which may lead to information extraction misled. In addition, they only consider structural features of HINs when modeling users and items during exploring HINs, which may lead to useful information for recommendation lost irreversibly. To address these problems, we propose a HIN based unified embedding model for recommendation, called HueRec. We assume there exist some common characteristics under different meta-paths for each user or item, and use data from all meta-paths to learn unified users’ and items’ representations. So the interrelation between meta-paths are utilized to alleviate the problems of data sparsity and noises on one meta-path. Different from existing models which first explore HINs then make recommendations, we combine these two parts into an end-to-end model to avoid useful information lost in initial phases. In addition, we embed all users, items and meta-paths into related latent spaces. Therefore, we can measure users’ preferences on meta-paths to improve the performances of personalized recommendation. Extensive experiments show HueRec consistently outperforms state-of-the-art methods.

124. Neural News Recommendation with Attentive Multi-View Learning [PDF] 返回目录
IJCAI 2019.
Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie
Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods usually learn these representations based on single news information, e.g., title, which may be insufficient. In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information. The core of our approach is a news encoder and a user encoder. In the news encoder we propose an attentive multi-view learning model to learn unified news representations from titles, bodies and topic categories by regarding them as different views of news. In addition, we apply both word-level and view-level attention mechanism to news encoder to select important words and views for learning informative news representations. In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of news recommendation.

125. PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation [PDF] 返回目录
IJCAI 2019.
Qiong Wu, Yong Liu, Chunyan Miao, Binqiang Zhao, Yin Zhao, Lu Guan
This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations. Specifically, for each user, a generator recommends a set of diverse and relevant items by sequentially sampling from a personalized Determinantal Point Process (DPP) kernel matrix. This kernel matrix is constructed by two learnable components: the general co-occurrence of diverse items and the user's personal preference to items. To learn the first component, we propose a novel pairwise learning paradigm using training pairs, and each training pair consists of a set of diverse items and a set of similar items randomly sampled from the observed data of all users. The second component is learnt through adversarial training against a discriminator which strives to distinguish between recommended items and the ground-truth sets randomly sampled from the observed data of the target user. Experimental results show that PD-GAN is superior to generate recommendations that are both diverse and relevant.

126. BPAM: Recommendation Based on BP Neural Network with Attention Mechanism [PDF] 返回目录
IJCAI 2019.
Wu-Dong Xi, Ling Huang, Chang-Dong Wang, Yin-Yu Zheng, Jianhuang Lai
Inspired by the significant success of deep learning, some attempts have been made to introduce deep neural networks (DNNs) in recommendation systems to learn users' preferences for items. Since DNNs are well suitable for representation learning, they enable recommendation systems to generate more accurate prediction. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily lead to over-fitting. To tackle these problems, we propose a novel recommendation algorithm based on Back Propagation (BP) neural network with Attention Mechanism (BPAM). In particular, the BP neural network is utilized to learn the complex relationship of the target users and their neighbors. Compared with deep neural network, the shallow neural network, i.e., BP neural network, can not only reduce the computational and storage costs, but also prevent the model from over-fitting. In addition, an attention mechanism is designed to capture the global impact on all nearest target users for each user. Extensive experiments on eight benchmark datasets have been conducted to evaluate the effectiveness of the proposed model.

127. CFM: Convolutional Factorization Machines for Context-Aware Recommendation [PDF] 返回目录
IJCAI 2019.
Xin Xin, Bo Chen, Xiangnan He, Dong Wang, Yue Ding, Joemon Jose
Factorization Machine (FM) is an effective solution for context-aware recommender systems (CARS) which models second-order feature interactions by inner product. However, it is insufficient to capture high-order and nonlinear interaction signals. While several recent efforts have enhanced FM with neural networks, they assume the embedding dimensions are independent from each other and model high-order interactions in a rather implicit manner. In this paper, we propose Convolutional Factorization Machine (CFM) to address above limitations. Specifically, CFM models second-order interactions with outer product, resulting in ''images'' which capture correlations between embedding dimensions. Then all generated ''images'' are stacked, forming an interaction cube. 3D convolution is applied above it to learn high-order interaction signals in an explicit approach. Besides, we also leverage a self-attention mechanism to perform the pooling of features to reduce time complexity. We conduct extensive experiments on three real-world datasets, demonstrating significant improvement of CFM over competing methods for context-aware top-k recommendation.

128. Graph Contextualized Self-Attention Network for Session-based Recommendation [PDF] 返回目录
IJCAI 2019.
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou
Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming). Recently, Self-Attention Network (SAN) has achieved significant success in various sequence modeling tasks without using either recurrent or convolutional network. However, SAN lacks local dependencies that exist over adjacent items and limits its capacity for learning contextualized representations of items in sequences. In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation. In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN). Then each session learns long-range dependencies by applying the self-attention mechanism. Finally, each session is represented as a linear combination of the global preference and the current interest of that session. Extensive experiments on two real-world datasets show that GC-SAN outperforms state-of-the-art methods consistently.

129. Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation [PDF] 返回目录
IJCAI 2019.
Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, Xing Xie
User modeling is an essential task for online recommender systems. In the past few decades, collaborative filtering (CF) techniques have been well studied to model users' long term preferences. Recently, recurrent neural networks (RNN) have shown a great advantage in modeling users' short term preference. A natural way to improve the recommender is to combine both long-term and short-term modeling. Previous approaches neglect the importance of dynamically integrating these two user modeling paradigms. Moreover, users' behaviors are much more complex than sentences in language modeling or images in visual computing, thus the classical structures of RNN such as Long Short-Term Memory (LSTM) need to be upgraded for better user modeling. In this paper, we improve the traditional RNN structure by proposing a time-aware controller and a content-aware controller, so that contextual information can be well considered to control the state transition. We further propose an attention-based framework to combine users' long-term and short-term preferences, thus users' representation can be generated adaptively according to the specific context. We conduct extensive experiments on both public and industrial datasets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.

130. DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns [PDF] 返回目录
IJCAI 2019.
Feng Yuan, Lina Yao, Boualem Benatallah
Cross-domain recommendation has long been one of the major topics in recommender systems.Recently, various deep models have been proposed to transfer the learned knowledge across domains, but most of them focus on extracting abstract transferable features from auxilliary contents, e.g., images and review texts, and the patterns in the rating matrix itself is rarely touched. In this work, inspired by the concept of domain adaptation, we proposed a deep domain adaptation model (DARec) that is capable of extracting and transferring patterns from rating matrices only without relying on any auxillary information. We empirically demonstrate on public datasets that our method achieves the best performance among several state-of-the-art alternative cross-domain recommendation models.

131. STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems [PDF] 返回目录
IJCAI 2019.
Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN.

132. Quaternion Collaborative Filtering for Recommendation [PDF] 返回目录
IJCAI 2019.
Shuai Zhang, Lina Yao, Lucas Vinh Tran, Aston Zhang, Yi Tay
This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation. Our proposed QCF relies on and exploits computation with Quaternion algebra, benefiting from the expressiveness and rich representation learning capability of Hamilton products. Quaternion representations, based on hypercomplex numbers, enable rich inter-latent dependencies between imaginary components. This encourages intricate relations to be captured when learning user-item interactions, serving as a strong inductive bias as compared with the real-space inner product. All in all, we conduct extensive experiments on six real-world datasets, demonstrating the effectiveness of Quaternion algebra in recommender systems. The results exhibit that QCF outperforms a wide spectrum of strong neural baselines on all datasets. Ablative experiments confirm the effectiveness of Hamilton-based composition over multi-embedding composition in real space.

133. Feature-level Deeper Self-Attention Network for Sequential Recommendation [PDF] 返回目录
IJCAI 2019.
Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, Xiaofang Zhou
Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. In this paper, we propose a novel method named Feature-level Deeper Self-Attention Network (FDSA) for sequential recommendation. Specifically, FDSA first integrates various heterogeneous features of items into feature sequences with different weights through a vanilla mechanism. After that, FDSA applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Then, we integrate the outputs of these two blocks to a fully-connected layer for next item recommendation. Finally, comprehensive experimental results demonstrate that considering the transition relationships between features can significantly improve the performance of sequential recommendation.

134. Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems [PDF] 返回目录
IJCAI 2019.
Xiao Zhou, Danyang Liu, Jianxun Lian, Xing Xie
The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple types of user feedback provide us with tremendous opportunities to detect individuals' fine-grained preferences. Different from most existing recommender systems that rely on a single type of feedback, we advocate incorporating multiple types of user-item interactions for better recommendations. Based on the observation that the underlying spectrum of user preferences is reflected in various types of interactions with items and can be uncovered by latent relational learning in metric space, we propose a unified neural learning framework, named Multi-Relational Memory Network (MRMN). It can not only model fine-grained user-item relations but also enable us to discriminate between feedback types in terms of the strength and diversity of user preferences. Extensive experiments show that the proposed MRMN model outperforms competitive state-of-the-art algorithms in a wide range of scenarios, including e-commerce, local services, and job recommendations.

135. Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation [PDF] 返回目录
IJCAI 2019.
Yanan Xu, Yanmin Zhu, Yanyan Shen, Jiadi Yu
Collaborative Filtering (CF) is among the most successful techniques in recommendation tasks. Recent works have shown a boost of performance of CF when introducing the pairwise relationships between users and items or among items (users) using interaction data. However, these works usually only utilize one kind of information, i.e., user preference in a user-item interaction matrix or item dependency in interaction sequences which can limit the recommendation performance. In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph). We design a novel graph convolutional network (PGCN) to learn shared representations of users and items with the three heterogeneous graphs. In our approach, a neighbor pooling and a convolution operation are designed to aggregate features of neighbors. Extensive experiments on two real-world datasets demonstrate that our graph convolution approaches outperform various competitive methods in terms of two metrics, and the heterogeneous graphs are proved effective for improving recommendation performance.

136. Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation [PDF] 返回目录
IJCAI 2019.
Shengze Yu, Xin Wang, Wenwu Zhu, Peng Cui, Jingdong Wang
Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.

137. Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach [PDF] 返回目录
IJCAI 2019.
Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W. Zheng, Qi Liu
In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck collar). Nevertheless, most previous fashion recommendation models comprehend the clothing images with a global content representation and lack detailed understanding of users' semantic preferences, which usually leads to inferior recommendation performance. To bridge this gap, we propose a novel Semantic Attribute Explainable Recommender System (SAERS). Specifically, we first introduce a fine-grained interpretable semantic space. We then develop a Semantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA) module to project users and items into this space, respectively. With SAERS, we are capable of not only providing cloth recommendations for users, but also explaining the reason why we recommend the cloth through intuitive visual attribute semantic highlights in a personalized manner. Extensive experiments conducted on real-world datasets clearly demonstrate the effectiveness of our approach compared with the state-of-the-art methods.

138. Novel Collaborative Filtering Recommender Friendly to Privacy Protection [PDF] 返回目录
IJCAI 2019.
Jun Wang, Qiang Tang, Afonso Arriaga, Peter Y. A. Ryan
Nowadays, recommender system is an indispensable tool in many information services, and a large number of algorithms have been designed and implemented. However, fed with very large datasets, state-of-the-art recommendation algorithms often face an efficiency bottleneck, i.e., it takes huge amount of computing resources to train a recommendation model. In order to satisfy the needs of privacy-savvy users who do not want to disclose their information to the service provider, the complexity of most existing solutions becomes prohibitive. As such, it is an interesting research question to design simple and efficient recommendation algorithms that achieve reasonable accuracy and facilitate privacy protection at the same time.In this paper, we propose an efficient recommendation algorithm, named CryptoRec, which has two nice properties: (1) can estimate a new user's preferences by directly using a model pre-learned from an expert dataset, and the new user's data is not required to train the model; (2) can compute recommendations with only addition and multiplication operations. As to the evaluation, we first test the recommendation accuracy on three real-world datasets and show that CryptoRec is competitive with state-of-the-art recommenders. Then, we evaluate the performance of the privacy-preserving variants of CryptoRec and show that predictions can be computed in seconds on a PC. In contrast, existing solutions will need tens or hundreds of hours on more powerful computers.

139. ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation [PDF] 返回目录
IJCAI 2019.
Jing Song, Hong Shen, Zijing Ou, Junyi Zhang, Teng Xiao, Shangsong Liang
Session-based recommendation is a challenging problem due to the inherent uncertainty of user behavior and the limited historical click information. Latent factors and the complex dependencies within the user’s current session have an important impact on the user's main intention, but the existing methods do not explicitly consider this point. In this paper, we propose a novel model, Interest Shift and Latent Factors Combination Model (ISLF), which can capture the user's main intention by taking into account the user’s interest shift (i.e. long-term and short-term interest) and latent factors simultaneously. In addition, we experimentally give an explicit explanation of this combination in our ISLF. Our experimental results on three benchmark datasets show that our model achieves state-of-the-art performance on all test datasets.

140. Pre-training of Graph Augmented Transformers for Medication Recommendation [PDF] 返回目录
IJCAI 2019.
Junyuan Shang, Tengfei Ma, Cao Xiao, Jimeng Sun
Medication recommendation is an important healthcare application. It is commonly formulated as a temporal prediction task. Hence, most existing works only utilize longitudinal electronic health records (EHRs) from a small number of patients with multiple visits ignoring a large number of patients with a single visit (selection bias). Moreover, important hierarchical knowledge such as diagnosis hierarchy is not leveraged in the representation learning process. Despite the success of deep learning techniques in computational phenotyping, most previous approaches have two limitations: task-oriented representation and ignoring hierarchies of medical codes. To address these challenges, we propose G-BERT, a new model to combine the power of Graph Neural Networks (GNNs) and BERT (Bidirectional Encoder Representations from Transformers) for medical code representation and medication recommendation. We use GNNs to represent the internal hierarchical structures of medical codes. Then we integrate the GNN representation into a transformer-based visit encoder and pre-train it on EHR data from patients only with a single visit. The pre-trained visit encoder and representation are then fine-tuned for downstream predictive tasks on longitudinal EHRs from patients with multiple visits. G-BERT is the first to bring the language model pre-training schema into the healthcare domain and it achieved state-of-the-art performance on the medication recommendation task.

141. Impact of Consuming Suggested Items on the Assessment of Recommendations in User Studies on Recommender Systems [PDF] 返回目录
IJCAI 2019.
Benedikt Loepp, Tim Donkers, Timm Kleemann, Jürgen Ziegler
User studies are increasingly considered important in research on recommender systems. Although participants typically cannot consume any of the recommended items, they are often asked to assess the quality of recommendations and of other aspects related to user experience by means of questionnaires. Not being able to listen to recommended songs or to watch suggested movies, might however limit the validity of the obtained results. Consequently, we have investigated the effect of consuming suggested items. In two user studies conducted in different domains, we showed that consumption may lead to differences in the assessment of recommendations and in questionnaire answers. Apparently, adequately measuring user experience is in some cases not possible without allowing users to consume items. On the other hand, participants sometimes seem to approximate the actual value of recommendations reasonably well depending on domain and provided information.

142. Causal Embeddings for Recommendation: An Extended Abstract [PDF] 返回目录
IJCAI 2019.
Flavian Vasile, Stephen Bonner
Recommendations are commonly used to modifyuser’s natural behavior, for example, increasingproduct sales or the time spent on a website. Thisresults in a gap between the ultimate business ob-jective and the classical setup where recommenda-tions are optimized to be coherent with past user be-havior. To bridge this gap, we propose a new learn-ing setup for recommendation that optimizes for theIncremental Treatment Effect (ITE) of the policy.We show this is equivalent to learning to predictrecommendation outcomes under a fully randomrecommendation policy and propose a new domainadaptation algorithm that learns from logged datacontaining outcomes from a biased recommenda-tion policy and predicts recommendation outcomesaccording to random exposure. We compare ourmethod against state-of-the-art factorization meth-ods, in addition to new approaches of causal rec-ommendation and show significant improvements.

143. Sequential Recommender Systems: Challenges, Progress and Prospects [PDF] 返回目录
IJCAI 2019.
Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, Mehmet A. Orgun
The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users’ preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area.

144. DeepRec: An Open-source Toolkit for Deep Learning based Recommendation [PDF] 返回目录
IJCAI 2019.
Shuai Zhang, Yi Tay, Lina Yao, Bin Wu, Aixin Sun
Deep learning based recommender systems have been extensively explored in recent years. However, the large number of models proposed each year poses a big challenge for both researchers and practitioners in reproducing the results for further comparisons. Although a portion of papers provides source code, they adopted different programming languages or different deep learning packages, which also raises the bar in grasping the ideas. To alleviate this problem, we released the open source project: \textbf{DeepRec}. In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow. Three major recommendation scenarios: rating prediction, top-N recommendation (item ranking) and sequential recommendation, were considered. Meanwhile, DeepRec maintains good modularity and extensibility to easily incorporate new models into the framework. It is distributed under the terms of the GNU General Public License. The source code is available at github: https://github.com/cheungdaven/DeepRec

145. Hierarchical User and Item Representation with Three-Tier Attention for Recommendation [PDF] 返回目录
NAACL 2019.
Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang
Utilizing reviews to learn user and item representations is useful for recommender systems. Existing methods usually merge all reviews from the same user or for the same item into a long document. However, different reviews, sentences and even words usually have different informativeness for modeling users and items. In this paper, we propose a hierarchical user and item representation model with three-tier attention to learn user and item representations from reviews for recommendation. Our model contains three major components, i.e., a sentence encoder to learn sentence representations from words, a review encoder to learn review representations from sentences, and a user/item encoder to learn user/item representations from reviews. In addition, we incorporate a three-tier attention network in our model to select important words, sentences and reviews. Besides, we combine the user and item representations learned from the reviews with user and item embeddings based on IDs as the final representations to capture the latent factors of individual users and items. Extensive experiments on four benchmark datasets validate the effectiveness of our approach.

146. Recommendations for Datasets for Source Code Summarization [PDF] 返回目录
NAACL 2019.
Alexander LeClair, Collin McMillan
Source Code Summarization is the task of writing short, natural language descriptions of source code. The main use for these descriptions is in software documentation e.g. the one-sentence Java method descriptions in JavaDocs. Code summarization is rapidly becoming a popular research problem, but progress is restrained due to a lack of suitable datasets. In addition, a lack of community standards for creating datasets leads to confusing and unreproducible research results – we observe swings in performance of more than 33% due only to changes in dataset design. In this paper, we make recommendations for these standards from experimental results. We release a dataset based on prior work of over 2.1m pairs of Java methods and one sentence method descriptions from over 28k Java projects. We describe the dataset and point out key differences from natural language data, to guide and support future researchers.

147. Joint Optimization of Tree-based Index and Deep Model for Recommender Systems [PDF] 返回目录
NeurIPS 2019.
Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai
Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution. The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index. By indexing items in a tree hierarchy and training a user-node preference prediction model satisfying a max-heap like property in the tree, TDM provides logarithmic computational complexity w.r.t. the corpus size, enabling the use of arbitrary advanced models in candidate retrieval and recommendation. In tree-based recommendation methods, the quality of both the tree index and the user-node preference prediction model determines the recommendation accuracy for the most part. We argue that the learning of tree index and preference model has interdependence. Our purpose, in this paper, is to develop a method to jointly learn the index structure and user preference prediction model. In our proposed joint optimization framework, the learning of index and user preference prediction model are carried out under a unified performance measure. Besides, we come up with a novel hierarchical user preference representation utilizing the tree index hierarchy. Experimental evaluations with two large-scale real-world datasets show that the proposed method improves recommendation accuracy significantly. Online A/B test results at a display advertising platform also demonstrate the effectiveness of the proposed method in production environments.

148. Learning Disentangled Representations for Recommendation [PDF] 返回目录
NeurIPS 2019.
Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, Wenwu Zhu
User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users’ decision making processes. The factors are highly entangled, and may range from high-level ones that govern user intentions, to low-level ones that characterize a user’s preference when executing an intention. Learning representations that uncover and disentangle these latent factors can bring enhanced robustness, interpretability, and controllability. However, learning such disentangled representations from user behavior is challenging, and remains largely neglected by the existing literature. In this paper, we present the MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE) for learning disentangled representations from user behavior. Our approach achieves macro disentanglement by inferring the high-level concepts associated with user intentions (e.g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately. A micro-disentanglement regularizer, stemming from an information-theoretic interpretation of VAEs, then forces each dimension of the representations to independently reflect an isolated low-level factor (e.g., the size or the color of a shirt). Empirical results show that our approach can achieve substantial improvement over the state-of-the-art baselines. We further demonstrate that the learned representations are interpretable and controllable, which can potentially lead to a new paradigm for recommendation where users are given fine-grained control over targeted aspects of the recommendation lists.

149. A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation [PDF] 返回目录
NeurIPS 2019.
Xueying Bai, Jian Guan, Hongning Wang
Reinforcement learning is effective in optimizing policies for recommender systems. Current solutions mostly focus on model-free approaches, which require frequent interactions with a real environment, and thus are expensive in model learning. Offline evaluation methods, such as importance sampling, can alleviate such limitations, but usually request a large amount of logged data and do not work well when the action space is large. In this work, we propose a model-based reinforcement learning solution which models the user-agent interaction for offline policy learning via a generative adversarial network. To reduce bias in the learnt policy, we use the discriminator to evaluate the quality of generated sequences and rescale the generated rewards. Our theoretical analysis and empirical evaluations demonstrate the effectiveness of our solution in identifying patterns from given offline data and learning policies based on the offline and generated data.

150. Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning [PDF] 返回目录
NeurIPS 2019.
Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen
Text-based interactive recommendation provides richer user preferences and has demonstrated advantages over traditional interactive recommender systems. However, recommendations can easily violate preferences of users from their past natural-language feedback, since the recommender needs to explore new items for further improvement. To alleviate this issue, we propose a novel constraint-augmented reinforcement learning (RL) framework to efficiently incorporate user preferences over time. Specifically, we leverage a discriminator to detect recommendations violating user historical preference, which is incorporated into the standard RL objective of maximizing expected cumulative future rewards. Our proposed framework is general and is further extended to the task of constrained text generation. Empirical results show that the proposed method yields consistent improvement relative to standard RL methods.

151. Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization [PDF] 返回目录
AAAI 2018. AAAI18 - Artificial Intelligence and the Web
Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li
Points of interest (POI) recommendation has been drawn much attention recently due to the increasing popularity of location-based networks, e.g., Foursquare and Yelp. Among the existing approaches to POI recommendation, Matrix Factorization (MF) based techniques have proven to be effective. However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices. (2) Privacy issues: the users' preferences are at risk of leaking to malicious attackers via the centralized learner. To solve these, we present a Decentralized MF (DMF) framework for POI recommendation. Specifically, instead of maintaining all the low rank matrices and sensitive rating data for training, we propose a random walk based decentralized training technique to train MF models on each user's end, e.g., cell phone and Pad. By doing so, the ratings of each user are still kept on one's own hand, and moreover, decentralized learning can be taken as distributed learning with multi-learners (users), and thus alleviates the computation and storage issue. Experimental results on two real-world datasets demonstrate that, comparing with the classic and state-of-the-art latent factor models, DMF significantly improvements the recommendation performance in terms of precision and recall.

152. Social Recommendation with an Essential Preference Space [PDF] 返回目录
AAAI 2018. AAAI18 - Artificial Intelligence and the Web
Chun-Yi Liu, Chuan Zhou, Jia Wu, Yue Hu, Li Guo
Social recommendation, which aims to exploit social information to improve the quality of a recommender system, has attracted an increasing amount of attention in recent years. A large portion of existing social recommendation models are based on the tractable assumption that users consider the same factors to make decisions in both recommender systems and social networks. However, this assumption is not in concert with real-world situations, since users usually show different preferences in different scenarios. In this paper, we investigate how to exploit the differences between user preference in recommender systems and that in social networks, with the aim to further improve the social recommendation. In particular, we assume that the user preferences in different scenarios are results of different linear combinations from a more underlying user preference space. Based on this assumption, we propose a novel social recommendation framework, called social recommendation with an essential preferences space (SREPS), which simultaneously models the structural information in the social network, the rating and the consumption information in the recommender system under the capture of essential preference space. Experimental results on four real-world datasets demonstrate the superiority of the proposed SREPS model compared with seven state-of-the-art social recommendation methods.

153. Confidence-Aware Matrix Factorization for Recommender Systems [PDF] 返回目录
AAAI 2018. AAAI18 - Artificial Intelligence and the Web
Chao Wang, Qi Liu, Run-ze Wu, Enhong Chen, Chuanren Liu, Xunpeng Huang, Zhenya Huang
Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been widely used in recommender systems. The literature has reported that matrix factorization methods often produce superior accuracy of rating prediction in recommender systems. However, existing matrix factorization methods rarely consider confidence of the rating prediction and thus cannot support advanced recommendation tasks. In this paper, we propose a Confidence-aware Matrix Factorization (CMF) framework to simultaneously optimize the accuracy of rating prediction and measure the prediction confidence in the model. Specifically, we introduce variance parameters for both users and items in the matrix factorization process. Then, prediction interval can be computed to measure confidence for each predicted rating. These confidence quantities can be used to enhance the quality of recommendation results based on Confidence-aware Ranking (CR). We also develop two effective implementations of our framework to compute the confidence-aware matrix factorization for large-scale data. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness of our framework from multiple perspectives.

154. Personalized Time-Aware Tag Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - Artificial Intelligence and the Web
Keqiang Wang, Yuanyuan Jin, Haofen Wang, Hongwei Peng, Xiaoling Wang
Personalized tag recommender systems suggest a list of tags to a user when he or she wants to annotate an item. They utilize users’ preferences and the features of items. Tensorfactorization techniques have been widely used in tag recommendation. Given the user-item pair, although the classic PITF (Pairwise Interaction Tensor Factorization) explicitly models the pairwise interactions among users, items and tags, it overlooks users’ short-term interests and suffers from data sparsity. On the other hand, given the user-item-time triple, time-aware approaches like BLL (Base-Level Learning) utilize the time effect to capture the temporal dynamics and the most popular tags on items to handle cold start situation of new users. However, it works only on individual level and the target resource level, which cannot find users’ potential interests. In this paper, we propose an unified tag recommendation approach by considering both time awareness and personalization aspects, which extends PITF by adding weightsto user-tag interaction and item-tag interaction respectively. Compared to PITF, our proposed model can depict temporal factor by temporal weights and relieve data sparsity problem by referencing the most popular tags on items. Further, our model brings collaborative filtering (CF) to time-aware models, which can mine information from global data and help improving the ability of recommending new tags. Different from the power-form functions used in the existing time aware recommendation models, we use the Hawkes process with the exponential intensity function to improve the model’s efficiency. The experimental results show that our proposed model outperforms the state of the art tag recommendation methods in accuracy and has better ability to recommend new tags.

155. Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems [PDF] 返回目录
AAAI 2018. AAAI18 - Artificial Intelligence and the Web
Yu Wang, Jixing Xu, Aohan Wu, Mantian Li, Yang He, Jinghe Hu, Weipeng P. Yan
Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. To our best knowledge, the complex brain activity mechanism behind human shopping activities is never considered in existing recommender systems. From a human vision perspective, we found two key factors that affect users’ behaviors: items’ attractiveness and their matching degrees with users’ interests. This paper proposes Telepath, a vision-based bionic recommender system model, which simulates human brain activities in decision making of shopping, thus understanding users from such perspective. The core of Telepath is a complex deep neural network with multiple subnetworks. In practice, the Telepath model has been launched to JD’s recommender system and advertising system and outperformed the former state-of-the-art method. For one of the major item recommendation blocks on the JD app, click-through rate (CTR), gross merchandise value (GMV) and orders have been increased 1.59%, 8.16% and 8.71% respectively by Telepath. For several major ad publishers of JD demand-side platform, CTR, GMV and return on investment have been increased 6.58%, 61.72% and 65.57% respectively by the first launch of Telepath, and further increased 2.95%, 41.75% and 41.37% respectively by the second launch.

156. Compatibility Family Learning for Item Recommendation and Generation [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Applications
Yong-Siang Shih, Kai-Yueh Chang, Hsuan-Tien Lin, Min Sun
Compatibility between items, such as clothes and shoes, is a major factor among customer's purchasing decisions. However, learning "compatibility" is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed. We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space. These prototypes reflect the broad notions of compatibility. We refer to both the embedding and prototypes as "Compatibility Family." In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item. We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset. Our method consistently achieves state-of-the-art performance. Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item. We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items. Our generated images are significantly preferred, with roughly twice the number of votes as others.

157. Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Applications
Menghan Wang, Xiaolin Zheng, Yang Yang, Kun Zhang
This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, however, may not hold true due to various motivations of making online friends and dynamics of online social networks. Inspired by recent causal process based recommendations that first model user exposures towards items and then use these exposures to guide rating prediction, we utilize social information to capture user exposures rather than user preferences. We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality. Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering. We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures. Experiments on four real-world datasets demonstrate that our methods outperform the state-of-the-art methods on top-N recommendations. Further study compares the robustness and scalability of the two proposed methods.

158. Attention-Based Transactional Context Embedding for Next-Item Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Applications
Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, Wei Liu
To recommend the next item to a user in a transactional context is practical yet challenging in applications such as marketing campaigns. Transactional context refers to the items that are observable in a transaction. Most existing transaction based recommender systems (TBRSs) make recommendations by mainly considering recently occurring items instead of all the ones observed in the current context. Moreover, they often assume a rigid order between items within a transaction, which is not always practical. More importantly, a long transaction often contains many items irreverent to the next choice, which tends to overwhelm the influence of a few truly relevant ones. Therefore, we posit that a good TBRS should not only consider all the observed items in the current transaction but also weight them with different relevance to build an attentive context that outputs the proper next item with a high probability. To this end, we design an effective attention based transaction embedding model (ATEM) for context embedding to weight each observed item in a transaction without assuming order. The empirical study on real-world transaction datasets proves that ATEM significantly outperforms the state-of-the-art methods in terms of both accuracy and novelty.

159. WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Applications
Lu Yu, Chuxu Zhang, Shichao Pei, Guolei Sun, Xiangliang Zhang
Top-N item recommendation techniques, e.g., pairwise models, learn the rank of users' preferred items through separating items into positive samples if user-item interactions exist, and negative samples otherwise. This separation results in an important issue: the extreme imbalance between positive and negative samples, because the number of items with user actions is much less than those without actions. The problem is even worse for "cold-start" users. In addition, existing learning models only consider the observed user-item proximity, while neglecting other useful relations, such as the unobserved but potentially helpful user-item relations, and high-order proximity in user-user, item-item relations. In this paper, we aim at incorporating multiple types of user-item relations into a unified pairwise ranking model towards approximately optimizing ranking metrics mean average precision (MAP), and mean reciprocal rank (MRR). Instead of taking statical separation of positive and negative sets, we employ a random walk approach to dynamically draw positive samples from short random walk sequences, and a rank-aware negative sampling method to draw negative samples for efficiently learning the proposed pairwise ranking model. The proposed method is compared with several state-of-the-art baselines on two large and sparse datasets. Experimental results show that our proposed model outperforms the other baselines with average 4% at different top-N metrics, in particular for cold-start users with 6% on average.

160. Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Methods
Trong Dinh Thac Do, Longbing Cao
Modelling sparse and large data sets is highly in demand yet challenging in recommender systems. With the computation only on the non-zero ratings, Poisson Factorization (PF) enabled by variational inference has shown its high efficiency in scalable recommendation, e.g., modeling millions of ratings. However, as PF learns the ratings by individual users on items with the Gamma distribution, it cannot capture the coupling relations between users (items) and the rating popularity (i.e., favorable rating scores that are given to one item) and rating sparsity (i.e., those users (items) with many zero ratings) for one item (user). This work proposes Coupled Poisson Factorization (CPF) to learn the couplings between users (items), and the user/item attributes (i.e., metadata) are integrated into CPF to form the Metadata-integrated CPF (mCPF) to not only handle sparse but also popular ratings in very large-scale data. Our empirical results show that the proposed models significantly outperform PF and address the key limitations in PF for scalable recommendation.

161. Transferable Contextual Bandit for Cross-Domain Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Methods
Bo Liu, Ying Wei, Yu Zhang, Zhixian Yan, Qiang Yang
Traditional recommendation systems (RecSys) suffer from two problems: the exploitation-exploration dilemma and the cold-start problem. One solution to solving the exploitation-exploration dilemma is the contextual bandit policy, which adaptively exploits and explores user interests. As a result, the contextual bandit policy achieves increased rewards in the long run. The contextual bandit policy, however, may cause the system to explore more than needed in the cold-start situations, which can lead to worse short-term rewards. Cross-domain RecSys methods adopt transfer learning to leverage prior knowledge in a source RecSys domain to jump start the cold-start target RecSys. To solve the two problems together, in this paper, we propose the first applicable transferable contextual bandit (TCB) policy for the cross-domain recommendation. TCB not only benefits the exploitation but also accelerates the exploration in the target RecSys. TCB's exploration, in turn, helps to learn how to transfer between different domains. TCB is a general algorithm for both homogeneous and heterogeneous domains. We perform both theoretical regret analysis and empirical experiments. The empirical results show that TCB outperforms the state-of-the-art algorithms over time.

162. Exploiting Emotion on Reviews for Recommender Systems [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Methods
Xuying Meng, Suhang Wang, Huan Liu, Yujun Zhang
Review history is widely used by recommender systems to infer users' preferences and help find the potential interests from the huge volumes of data, whereas it also brings in great concerns on the sparsity and cold-start problems due to its inadequacy. Psychology and sociology research has shown that emotion information is a strong indicator for users' preferences. Meanwhile, with the fast development of online services, users are willing to express their emotion on others' reviews, which makes the emotion information pervasively available. Besides, recent research shows that the number of emotion on reviews is always much larger than the number of reviews. Therefore incorporating emotion on reviews may help to alleviate the data sparsity and cold-start problems for recommender systems. In this paper, we provide a principled and mathematical way to exploit both positive and negative emotion on reviews, and propose a novel framework MIRROR, exploiting eMotIon on Reviews for RecOmmendeR systems from both global and local perspectives. Empirical results on real-world datasets demonstrate the effectiveness of our proposed framework and further experiments are conducted to understand how emotion on reviews works for the proposed framework.

163. Personalized Privacy-Preserving Social Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Methods
Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, Yujun Zhang
Privacy leakage is an important issue for social recommendation. Existing privacy preserving social recommendation approaches usually allow the recommender to fully control users' information. This may be problematic since the recommender itself may be untrusted, leading to serious privacy leakage. Besides, building social relationships requires sharing interests as well as other private information, which may lead to more privacy leakage. Although sometimes users are allowed to hide their sensitive private data using privacy settings, the data being shared can still be abused by the adversaries to infer sensitive private information. Supporting social recommendation with least privacy leakage to untrusted recommender and other users (i.e., friends) is an important yet challenging problem. In this paper, we aim to address the problem of achieving privacy-preserving social recommendation under personalized privacy settings. We propose PrivSR, a novel framework for privacy-preserving social recommendation, in which users can model ratings and social relationships privately. Meanwhile, by allocating different noise magnitudes to personalized sensitive and non-sensitive ratings, we can protect users' privacy against the untrusted recommender and friends. Theoretical analysis and experimental evaluation on real-world datasets demonstrate that our framework can protect users' privacy while being able to retain effectiveness of the underlying recommender system.

164. SAGA: A Submodular Greedy Algorithm for Group Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Methods
Shameem Ahamed Puthiya Parambath, Nishant Vijayakumar, Sanjay Chawla
In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users. The problem of group recommendation arises naturally in many real world contexts, and is closely related to the budgeted social choice problem studied in economics. We frame the group recommendation problem as choosing a subgraph with the largest group consensus score in a completely connected graph defined over the item affinity matrix. We propose a fast greedy algorithm with strong theoretical guarantees, and show that the proposed algorithm compares favorably to the state-of-the-art group recommendation algorithms according to commonly used relevance and coverage performance measures on benchmark dataset.

165. ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - Machine Learning Methods
Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, Jun Gao
A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, which may fail to fully represent the data itself due to limited human instinct. Recent works usually use RNN-based methods to give an overall embedding of a behavior sequence, which then could be exploited by the downstream applications. However, this can only preserve very limited information, or aggregated memories of a person. When a downstream application requires to facilitate the modeled user features, it may lose the integrity of the specific highly correlated behavior of the user, and introduce noises derived from unrelated behaviors. This paper proposes an attention based user behavior modeling framework called ATRank, which we mainly use for recommendation tasks. Heterogeneous user behaviors are considered in our model that we project all types of behaviors into multiple latent semantic spaces, where influence can be made among the behaviors via self-attention. Downstream applications then can use the user behavior vectors via vanilla attention. Experiments show that ATRank can achieve better performance and faster training process. We further explore ATRank to use one unified model to predict different types of user behaviors at the same time, showing a comparable performance with the highly optimized individual models.

166. Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation [PDF] 返回目录
AAAI 2018. AAAI18 - NLP and Text Mining
Xiaoyan Cai, Junwei Han, Libin Yang
Network representation has been recently exploited for many applications, such as citation recommendation, multi-label classification and link prediction. It learns low-dimensional vector representation for each vertex in networks. Existing network representation methods only focus on incomplete aspects of vertex information (i.e., vertex content, network structure or partial integration), moreover they are commonly designed for homogeneous information networks where all the vertices of a network are of the same type. In this paper, we propose a deep network representation model that integrates network structure and the vertex content information into a unified framework by exploiting generative adversarial network, and represents different types of vertices in the heterogeneous network in a continuous and common vector space. Based on the proposed model, we can obtain heterogeneous bibliographic network representation for efficient citation recommendation. The proposed model also makes personalized citation recommendation possible, which is a new issue that a few papers addressed in the past. When evaluated on the AAN and DBLP datasets, the performance of the proposed heterogeneous bibliographic network based citation recommendation approach is comparable with that of the other network representation based citation recommendation approaches. The results also demonstrate that the personalized citation recommendation approach is more effective than the non-personalized citation recommendation approach.

167. An E-Learning Recommender That Helps Learners Find the Right Materials [PDF] 返回目录
AAAI 2018. EAAI18 - Full Papers
Blessing Mbipom, Stewart Massie, Susan Craw
Learning materials are increasingly available on the Web making them an excellent source of information for building e-Learning recommendation systems. However, learners often have difficulty finding the right materials to support their learning goals because they lack sufficient domain knowledge to craft effective queries that convey what they wish to learn. The unfamiliar vocabulary often used by domain experts creates a semantic gap between learners and experts, and also makes it difficult to map a learner's query to relevant learning materials. We build an e-Learning recommender system that uses background knowledge extracted from a collection of teaching materials and encyclopedia sources to support the refinement of learners' queries. Our approach allows us to bridge the gap between learners and teaching experts. We evaluate our method using a collection of realistic learner queries and a dataset of Machine Learning and Data Mining documents. Evaluation results show our method to outperform benchmark approaches and demonstrates its effectiveness in assisting learners to find the right materials.

168. Deep Modeling of Social Relations for Recommendation [PDF] 返回目录
AAAI 2018. Student Abstracts
Wenqi Fan, Qing Li, Min Cheng
Social-based recommender systems have been recently proposed by incorporating social relations of users to alleviate sparsity issue of user-to-item rating data and to improve recommendation performance. Many of these social-based recommender systems linearly combine the multiplication of social features between users. However, these methods lack the ability to capture complex and intrinsic non-linear features from social relations. In this paper, we present a deep neural network based model to learn non-linear features of each user from social relations, and to integrate into probabilistic matrix factorization for rating prediction problem. Experiments demonstrate the advantages of the proposed method over state-of-the-art social-based recommender systems.

169. Explainable Cross-Domain Recommendations Through Relational Learning [PDF] 返回目录
AAAI 2018. Student Abstracts
Sirawit Sopchoke, Ken-ichi Fukui, Masayuki Numao
We propose a method to generate explainable recommendation rules on cross-domain problems. Our two main contributions are: i) using relational learning to generate the rules which are able to explain clearly why the items were recommended to the particular user, ii) using the user's preferences of items on different domains and item attributes to generate novel or unexpected recommendations for the user. To illustrate that our method is indeed feasible and applicable, we conducted experiments on music and movie domains.

170. TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation [PDF] 返回目录
ACL 2018. Long Papers
Alexander Fabbri, Irene Li, Prawat Trairatvorakul, Yijiao He, Weitai Ting, Robert Tung, Caitlin Westerfield, Dragomir Radev
The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources. In order to learn this dynamic field or stay up-to-date on the latest research, students as well as educators and researchers must constantly sift through multiple sources to find valuable, relevant information. To address this situation, we introduce TutorialBank, a new, publicly available dataset which aims to facilitate NLP education and research. We have manually collected and categorized over 5,600 resources on NLP as well as the related fields of Artificial Intelligence (AI), Machine Learning (ML) and Information Retrieval (IR). Our dataset is notably the largest manually-picked corpus of resources intended for NLP education which does not include only academic papers. Additionally, we have created both a search engine and a command-line tool for the resources and have annotated the corpus to include lists of research topics, relevant resources for each topic, prerequisite relations among topics, relevant sub-parts of individual resources, among other annotations. We are releasing the dataset and present several avenues for further research.

171. The Importance of Recommender and Feedback Features in a Pronunciation Learning Aid [PDF] 返回目录
ACL 2018. the 5th Workshop on Natural Language Processing Techniques for Educational Applications
Dzikri Fudholi, Hanna Suominen
Verbal communication — and pronunciation as its part — is a core skill that can be developed through guided learning. An artificial intelligence system can take a role in these guided learning approaches as an enabler of an application for pronunciation learning with a recommender system to guide language learners through exercises and feedback system to correct their pronunciation. In this paper, we report on a user study on language learners’ perceived usefulness of the application. 16 international students who spoke non-native English and lived in Australia participated. 13 of them said they need to improve their pronunciation skills in English because of their foreign accent. The feedback system with features for pronunciation scoring, speech replay, and giving a pronunciation example was deemed essential by most of the respondents. In contrast, a clear dichotomy between the recommender system perceived as useful or useless existed; the system had features to prompt new common words or old poorly-scored words. These results can be used to target research and development from information retrieval and reinforcement learning for better and better recommendations to speech recognition and speech analytics for accent acquisition.

172. WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages [PDF] 返回目录
COLING 2018.
Abhik Jana, Pranjal Kanojiya, Pawan Goyal, Animesh Mukherjee
The exponential increase in the usage of Wikipedia as a key source of scientific knowledge among the researchers is making it absolutely necessary to metamorphose this knowledge repository into an integral and self-contained source of information for direct utilization. Unfortunately, the references which support the content of each Wikipedia entity page, are far from complete. Why are the reference section ill-formed for most Wikipedia pages? Is this section edited as frequently as the other sections of a page? Can there be appropriate surrogates that can automatically enhance the reference section? In this paper, we propose a novel two step approach – WikiRef – that (i) leverages the wikilinks present in a scientific Wikipedia target page and, thereby, (ii) recommends highly relevant references to be included in that target page appropriately and automatically borrowed from the reference section of the wikilinks. In the first step, we build a classifier to ascertain whether a wikilink is a potential source of reference or not. In the following step, we recommend references to the target page from the reference section of the wikilinks that are classified as potential sources of references in the first step. We perform an extensive evaluation of our approach on datasets from two different domains – Computer Science and Physics. For Computer Science we achieve a notably good performance with a precision@1 of 0.44 for reference recommendation as opposed to 0.38 obtained from the most competitive baseline. For the Physics dataset, we obtain a similar performance boost of 10% with respect to the most competitive baseline.

173. Authorship Identification for Literary Book Recommendations [PDF] 返回目录
COLING 2018.
Haifa Alharthi, Diana Inkpen, Stan Szpakowicz
Book recommender systems can help promote the practice of reading for pleasure, which has been declining in recent years. One factor that influences reading preferences is writing style. We propose a system that recommends books after learning their authors’ style. To our knowledge, this is the first work that applies the information learned by an author-identification model to book recommendations. We evaluated the system according to a top-k recommendation scenario. Our system gives better accuracy when compared with many state-of-the-art methods. We also conducted a qualitative analysis by checking if similar books/authors were annotated similarly by experts.

174. LRMM: Learning to Recommend with Missing Modalities [PDF] 返回目录
EMNLP 2018.
Cheng Wang, Mathias Niepert, Hui Li
Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.

175. Content Explorer: Recommending Novel Entities for a Document Writer [PDF] 返回目录
EMNLP 2018.
Michal Lukasik, Richard Zens
Background research is an essential part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further research. Automated tools could help significantly in this discovery process and increase the productivity of the writer. In this paper, we formulate the problem of recommending topics to a writer. We consider this as a supervised learning problem and run a user study to validate this approach. We propose an evaluation metric and perform an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set. We demonstrate how a simple modification of the cross-entropy loss function leads to improved results of the deep learning models.

176. Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them [PDF] 返回目录
IJCAI 2018.
Antonio Rago, Oana Cocarascu, Francesca Toni
A significant problem of recommender systems is their inability to explain recommendations, resulting in turn in ineffective feedback from users and the inability to adapt to users’ preferences. We propose a hybrid method for calculating predicted ratings, built upon an item/aspect-based graph with users’ partially given ratings, that can be naturally used to provide explanations for recommendations, extracted from user-tailored Tripolar Argumentation Frameworks (TFs). We show that our method can be understood as a gradual semantics for TFs, exhibiting a desirable, albeit weak, property of balance. We also show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods, and illustrate how users can interact with the generated explanations to improve quality of recommendations.

177. Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation [PDF] 返回目录
IJCAI 2018.
Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim, Jaewoo Kang
Recommending a point-of-interest (POI) a user will visit next based on temporal and spatial context information is an important task in mobile-based applications. Recently, several POI recommendation models based on conventional sequential-data modeling approaches have been proposed. However, such models focus on only a user's check-in sequence information and the physical distance between POIs. Furthermore, they do not utilize the characteristics of POIs or the relationships between POIs. To address this problem, we propose CAPE, the first content-aware POI embedding model which utilizes text content that provides information about the characteristics of a POI. CAPE consists of a check-in context layer and a text content layer. The check-in context layer captures the geographical influence of POIs from the check-in sequence of a user, while the text content layer captures the characteristics of POIs from the text content. To validate the efficacy of CAPE, we constructed a large-scale POI dataset. In the experimental evaluation, we show that the performance of the existing POI recommendation models can be significantly improved by simply applying CAPE to the models.

178. DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation [PDF] 返回目录
IJCAI 2018.
Weiyu Cheng, Yanyan Shen, Yanmin Zhu, Linpeng Huang
Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this paper proposes a dual-embedding based deep latent factor model named DELF for recommendation with implicit feedback. In addition to learning a single embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users). We employ an attentive neural method to discriminate the importance of interacted users/items for dual-embedding learning. We further introduce a neural network architecture to incorporate dual embeddings for recommendation. A novel attempt of DELF is to model each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of DELF on item recommendation.

179. Improving Implicit Recommender Systems with View Data [PDF] 返回目录
IJCAI 2018.
Jingtao Ding, Guanghui Yu, Xiangnan He, Yuhan Quan, Yong Li, Tat-Seng Chua, Depeng Jin, Jiajie Yu
Most existing recommender systems leverage the primary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into implicit feedback based recommender systems (dubbed as Implicit Recommender Systems). We propose to model the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and ﬂexible than typical pointwise matrix factorization (MF) methods. However, such a pairwise formulation poses efﬁciency challenges in learning the model. To address this problem, we design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) learner. Notably, our algorithm can efﬁciently learn model parameters from the whole user-item matrix (including all missing data), with a rather low time complexity that is dependent on the observed data only. Extensive experiments on two real-world datasets demonstrate that our method outperforms several state-of-the-art MF methods by 10% ∼ 28.4%. Our implementation is available at: https://github.com/ dingjingtao/View_enhanced_ALS.

180. Recurrent Collaborative Filtering for Unifying General and Sequential Recommender [PDF] 返回目录
IJCAI 2018.
Disheng Dong, Xiaolin Zheng, Ruixun Zhang, Yan Wang
General recommender and sequential recommender are two commonly applied modeling paradigms for recommendation tasks. General recommender focuses on modeling the general user preferences, ignoring the sequential patterns in user behaviors; whereas sequential recommender focuses on exploring the item-to-item sequential relations, failing to model the global user preferences. In addition, better recommendation performance has recently been achieved by adopting an approach to combine them. However, previous approaches are unable to solve both tasks in a unified way and cannot capture the whole historical sequential information. In this paper, we propose a recommendation model named Recurrent Collaborative Filtering (RCF), which unifies both paradigms within a single model.Specifically, we combine recurrent neural network (the sequential recommender part) and matrix factorization model (the general recommender part) in a multi-task learning framework, where we perform joint optimization with shared model parameters enforcing the two parts to regularize each other. Furthermore, we empirically demonstrate on MovieLens and Netflix datasets that our model outperforms the state-of-the-art methods across the tasks of both sequential and general recommender.

181. Recommendation with Multi-Source Heterogeneous Information [PDF] 返回目录
IJCAI 2018.
Li Gao, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, Yue Hu
Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance.

182. Interpretable Recommendation via Attraction Modeling: Learning Multilevel Attractiveness over Multimodal Movie Contents [PDF] 返回目录
IJCAI 2018.
Liang Hu, Songlei Jian, Longbing Cao, Qingkui Chen
New contents like blogs and online videos are produced in every second in the new media age. We argue that attraction is one of the decisive factors for user selection of new contents. However, collaborative filtering cannot work without user feedback; and the existing content-based recommender systems are ineligible to capture and interpret the attractive points on new contents. Accordingly, we propose attraction modeling to learn and interpret user attractiveness. Specially, we build a multilevel attraction model (MLAM) over the content features -- the story (textual data) and cast members (categorical data) of movies. In particular, we design multilevel personal filters to calculate users' attractiveness on words, sentences and cast members at different levels. The experimental results show the superiority of MLAM over the state-of-the-art methods. In addition, a case study is provided to demonstrate the interpretability of MLAM by visualizing user attractiveness on a movie.

183. Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation [PDF] 返回目录
IJCAI 2018.
Duc-Trong Le, Hady W. Lauw, Yuan Fang
Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases). Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another supporting sequence (e.g., clicks). We develop three twin network structures modeling the generation of both target and support basket sequences. One based on "Siamese networks" facilitates full sharing of parameters between the two sequence types. The other two based on "fraternal networks" facilitate partial sharing of parameters. Experiments on real-world datasets show significant improvements upon baselines relying on one sequence type.

184. Discrete Factorization Machines for Fast Feature-based Recommendation [PDF] 返回目录
IJCAI 2018.
Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang
User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 107, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two real-world datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss.

185. Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback [PDF] 返回目录
IJCAI 2018.
Yong Liu, Lifan Zhao, Guimei Liu, Xinyan Lu, Peng Gao, Xiao-Li Li, Zhihui Jin
Matrix factorization has been widely adopted for recommendation by learning latent embeddings of users and items from observed user-item interaction data. However, previous methods usually assume the learned embeddings are static or homogeneously evolving with the same diffusion rate. This is not valid in most scenarios, where users’ preferences and item attributes heterogeneously drift over time. To remedy this issue, we have proposed a novel dynamic matrix factorization model, named Dynamic Bayesian Logistic Matrix Factorization (DBLMF), which aims to learn heterogeneous user and item embeddings that are drifting with inconsistent diffusion rates. More specifically, DBLMF extends logistic matrix factorization to model the probability a user would like to interact with an item at a given timestamp, and a diffusion process to connect latent embeddings over time. In addition, an efficient Bayesian inference algorithm has also been proposed to make DBLMF scalable on large datasets. The effectiveness of the proposed method has been demonstrated by extensive experiments on real datasets, compared with the state-of-the-art methods.

186. Your Tweets Reveal What You Like: Introducing Cross-media Content Information into Multi-domain Recommendation [PDF] 返回目录
IJCAI 2018.
Weizhi Ma, Min Zhang, Chenyang Wang, Cheng Luo, Yiqun Liu, Shaoping Ma
Cold start is a challenging problem in recommender systems. Many previous studies attempt to utilize extra information from other platforms to alleviate the problem. Most of the leveraged information is on-topic, directly related to users' preferences in the target domain. Thought to be unrelated, users' off-topic content information (such as user tweets) is usually omitted. However, the off-topic content information also helps to indicate the similarity of users on their tastes, interests, and opinions, which matches the underlying assumption of Collaborative Filtering (CF) algorithms. In this paper, we propose a framework to capture the features from user's off-topic content information in social media and introduce them into Matrix Factorization (MF) based algorithms. The framework is easy to understand and flexible in different embedding approaches and MF based algorithms. To the best of our knowledge, there is no previous study in which user's off-topic content in other platforms is taken into consideration. By capturing the cross-platform content including both on-topic and off-topic information, multiple algorithms with several embedding learning approaches have achieved significant improvements in rating prediction on three datasets. Especially in cold start scenarios, we observe greater enhancement. The results confirm our suggestion that off-topic cross-media information also contributes to the recommendation.

187. Matrix completion with Preference Ranking for Top-N Recommendation [PDF] 返回目录
IJCAI 2018.
Zengmao Wang, Yuhong Guo, Bo Du
Matrix completion has become a popular method for top-N recommendation due to the low rank nature of sparse rating matrices. However, many existing methods produce top-N recommendations by recovering a user-item matrix solely based on a low rank function or its relaxations, while ignoring other important intrinsic characteristics of the top-N recommendation tasks such as preference ranking over the items. In this paper, we propose a novel matrix completion method that integrates the low rank and preference ranking characteristics of recommendation matrix under a self-recovery model for top-N recommendation. The proposed method is formulated as a joint minimization problem and solved using an ADMM algorithm. We conduct experiments on E-commerce datasets. The experimental results show the proposed approach outperforms several state-of-the-art methods.

188. CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering [PDF] 返回目录
IJCAI 2018.
Quangui Zhang, Longbing Cao, Chengzhang Zhu, Zhiqiang Li, Jinguang Sun
Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as in- dependent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better ex- plain how and why a user has personalized pref- erence on an item. This work builds on non- IID learning to propose a neural user-item cou- pling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recom- menders: neural matrix factorization and Google’s Wide&Deep network.

189. PLASTIC: Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training [PDF] 返回目录
IJCAI 2018.
Wei Zhao, Benyou Wang, Jianbo Ye, Yongqiang Gao, Min Yang, Xiaojun Chen
Recommender systems provide users with ranked lists of items based on individual's preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term models represent the interactions between users and items that are supposed to change slowly across time, session-based models encode the information of users' interests and changing dynamics of items' attributes in short terms. In this paper, we propose a PLASTIC model, Prioritizing Long And Short-Term Information in top-n reCommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next item to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of items from the real list recorded. Extensive experiments show that our model exhibits significantly better performances on two widely used real-world datasets.

190. A Deep Framework for Cross-Domain and Cross-System Recommendations [PDF] 返回目录
IJCAI 2018.
Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet A. Orgun, Jia Wu
Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain or system to improve the recommendation accuracy in the target domain or system. Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy. However, this is a very challenging task because of the complex relationships between the latent factors of the source and target domains or systems. To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, we take into account the rating sparsity degrees of individual users and items in different domains or systems and use them to guide the DNN training process for utilizing the rating data more effectively. Extensive experiments conducted on three real-world datasets demonstrate that DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.

191. Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach [PDF] 返回目录
IJCAI 2018.
Jianxun Lian, Fuzheng Zhang, Xing Xie, Guangzhong Sun
Millions of news articles emerge every day. How to provide personalized news recommendations has become a critical task for service providers. In the past few decades, latent factor models has been widely used for building recommender systems (RSs). With the remarkable success of deep learning techniques especially in visual computing and natural language understanding, more and more researchers have been trying to leverage deep neural networks to learn latent representations for advanced RSs. Following mainstream deep learning-based RSs, we propose a novel deep fusion model (DFM), which aims to improve the representation learning abilities in deep RSs and can be used for both candidate retrieval and item re-ranking. There are two key components in our DFM approach, namely an inception module and an attention mechanism. The inception module improves the plain multi-layer network via leveraging of various levels of interaction simultaneously, while the attention mechanism merges latent representations learnt from different channels in a customized fashion. We conduct extensive experiments on a commercial news reading dataset, and the results demonstrate that the proposed DFM is superior to several state-of-the-art models.

192. LSTM Networks for Online Cross-Network Recommendations [PDF] 返回目录
IJCAI 2018.
Dilruk Perera, Roger Zimmermann
Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance. Existing models (1) fail to capture complex non-linear relationships in user interactions, and (2) are designed for offline settings hence, not updated online with incoming interactions to capture the dynamics in the recommender environment. We propose a novel multi-layered Long Short-Term Memory (LSTM) network based online solution to mitigate these issues. The proposed model contains three main extensions to the standard LSTM: First, an attention gated mechanism to capture long-term user preference changes. Second, a higher order interaction layer to alleviate data sparsity. Third, time aware LSTM cell gates to capture irregular time intervals between user interactions. We illustrate our solution using auxiliary information from Twitter and Google Plus to improve recommendations on YouTube. Extensive experiments show that the proposed model consistently outperforms state-of-the-art in terms of accuracy, diversity and novelty.

193. Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation [PDF] 返回目录
IJCAI 2018.
Hao Wang, Huawei Shen, Wentao Ouyang, Xueqi Cheng
Point-of-interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geo-susceptibility of POI, and their physical distance. Geo-influence captures POI?s capacity at exerting geographical influence to other POIs, and geo-susceptibility reflects POI?s propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation.

194. Sequential Recommender System based on Hierarchical Attention Networks [PDF] 返回目录
IJCAI 2018.
Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, Jian Wu
With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or item-item interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones.

195. Improving Entity Recommendation with Search Log and Multi-Task Learning [PDF] 返回目录
IJCAI 2018.
Jizhou Huang, Wei Zhang, Yaming Sun, Haifeng Wang, Ting Liu
Entity recommendation, providing search users with an improved experience by assisting them in finding related entities for a given query, has become an indispensable feature of today's Web search engine. Existing studies typically only consider the query issued at the current time step while ignoring the in-session preceding queries. Thus, they typically fail to handle the ambiguous queries such as "apple" because the model could not understand which apple (company or fruit) is talked about. In this work, we believe that the in-session contexts convey valuable evidences that could facilitate the semantic modeling of queries, and take that into consideration for entity recommendation. Furthermore, in order to better model the semantics of queries, we learn the model in a multi-task learning setting where the query representation is shared across entity recommendation and context-aware ranking. We evaluate our approach using large-scale, real-world search logs of a widely used commercial Web search engine. The experimental results show that incorporating context information significantly improves entity recommendation, and learning the model in a multi-task learning setting could bring further improvements.

196. Phrase Table as Recommendation Memory for Neural Machine Translation [PDF] 返回目录
IJCAI 2018.
Yang Zhao, Yining Wang, Jiajun Zhang, Chengqing Zong
Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently. However, several studies indicate that NMT often generates fluent but unfaithful translations. In this paper, we propose a method to alleviate this problem by using a phrase table as recommendation memory. The main idea is to add bonus to words worthy of recommendation, so that NMT can make correct predictions. Specifically, we first derive a prefix tree to accommodate all the candidate target phrases by searching the phrase translation table according to the source sentence.Then, we construct a recommendation word set by matching between candidate target phrases and previously translated target words by NMT. After that, we determine the specific bonus value for each recommendable word by using the attention vector and phrase translation probability. Finally,we integrate this bonus value into NMT to improve the translation results. The extensive experiments demonstrate that the proposed methods obtain remarkable improvements over the strong attention based NMT.

197. Metadata-dependent Infinite Poisson Factorization for Efficiently Modelling Sparse and Large Matrices in Recommendation [PDF] 返回目录
IJCAI 2018.
Trong Dinh Thac Do, Longbing Cao
Matrix Factorization (MF) is widely used in Recommender Systems (RSs) for estimating missing ratings in the rating matrix. MF faces major challenges of handling very sparse and large data. Poisson Factorization (PF) as an MF variant addresses these challenges with high efficiency by only computing on those non-missing elements. However, ignoring the missing elements in computation makes PF weak or incapable for dealing with columns or rows with very few observations (corresponding to sparse items or users). In this work, Metadata-dependent Poisson Factorization (MPF) is invented to address the user/item sparsity by integrating user/item metadata into PF. MPF adds the metadata-based observed entries to the factorized PF matrices. In addition, similar to MF, choosing the suitable number of latent components for PF is very expensive on very large datasets. Accordingly, we further extend MPF to Metadata-dependent Infinite Poisson Factorization (MIPF) that integrates Bayesian Nonparametric (BNP) technique to automatically tune the number of latent components. Our empirical results show that, by integrating metadata, MPF/MIPF significantly outperform the state-of-the-art PF models for sparse and large datasets. MIPF also effectively estimates the number of latent components.

198. Translation-based Recommendation: A Scalable Method for Modeling Sequential Behavior [PDF] 返回目录
IJCAI 2018.
Ruining He, Wang-Cheng Kang, Julian J. McAuley
Modeling the complex interactions between users and items is at the core of designing successful recommender systems. One key task consists of predicting users’ personalized sequential behavior, where the challenge mainly lies in modeling ‘third-order’ interactions between a user, her previously visited item(s), and the next item to consume. In this paper, we propose a unified method, TransRec, to model such interactions for largescale sequential prediction. Methodologically, we embed items into a ‘transition space’ where users are modeled as translation vectors operating on item sequences. Empirically, this approach outperforms the state-of-the-art on a wide spectrum of real-world datasets.

199. Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations [PDF] 返回目录
IJCAI 2018.
Xiaoying Zhang, Hong Xie, Junzhou Zhao, John C. S. Lui
The unbiasedness of online product ratings, an important property to ensure that users’ ratings indeed reflect their true evaluations to products, is vital both in shaping consumer purchase decisions and providing reliable recommendations. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to “discover” the distortions from historical ratings in each single rating (or at the micro-level), and perform the “debiasing operations” in real rating systems are the main objectives of this work.Using 42 million real customer ratings, we first show that users either “assimilate” or “contrast” to historical ratings under different scenarios: users conform to historical ratings if historical ratings are not far from the product quality (assimilation), while users deviate from historical ratings if historical ratings are significantly different from the product quality (contrast). This phenomenon can be explained by the well-known psychological argument: the “Assimilate-Contrast” theory. However, none of the existing works on modeling historical ratings’ influence have taken this into account, and this motivates us to propose the Histori- cal Influence Aware Latent Factor Model (HIALF), the first model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF also allows us to study the influence patterns of historical ratings from a modeling perspective, and it perfectly matches the assimilation and contrast effects we previously observed. Also, HIALF achieves significant improvements in predicting subsequent ratings, and accurately predicts the relationships revealed in previous empirical measurements on real ratings. Finally, we show that HIALF can contribute to better recommendations by decoupling users’ real preference from distorted ratings, and reveal the intrinsic product quality for wiser consumer purchase decisions.

200. Handling Uncertainty in Recommender Systems under the Belief Function Theory [PDF] 返回目录
IJCAI 2018.
Raoua Abdelkhalek
Dealing with uncertainty is an important challenge in real world applications including Recommender Systems (RSs). Different kinds of uncertainty can be pervaded at any level throughout the recommendation process, which follows to inaccurate results. The main goal of this research work is to consider RSs under an uncertain framework. We seek for an improvement over the traditional recommendation approaches in order to handle such uncertainty under the belief function theory.

201. Using Contextual Bandits with Behavioral Constraints for Constrained Online Movie Recommendation [PDF] 返回目录
IJCAI 2018.
Avinash Balakrishnan, Djallel Bouneffouf, Nicholas Mattei, Francesca Rossi
AI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. In many cases the rewards should not be the only guiding criteria, as there are additional constraints and/or priorities imposed by regulations, values, preferences, or ethical principles. We detail a novel online system, based on an extension of the contextual bandits framework, that learns a set of behavioral constraints by observation and uses these constraints as a guide when making decisions in an online setting while still being reactive to reward feedback. In addition, our system can highlight features of the context which are more predicted to be more rewarding and/or are in line with the behavioral constraints.Â We demonstrate the system by building an interactive interface for an online movie recommendation agent and show that our system is able to act within a set of behavior constraints without significantly degrading overall performance.

202. Content-Based Citation Recommendation [PDF] 返回目录
NAACL 2018. Long Papers
Chandra Bhagavatula, Sergey Feldman, Russell Power, Waleed Ammar
We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to distinguish between observed and unobserved citations. Unlike previous work, our method does not require metadata such as author names which can be missing, e.g., during the peer review process. Without using metadata, our method outperforms the best reported results on PubMed and DBLP datasets with relative improvements of over 18% in F1@20 and over 22% in MRR. We show empirically that, although adding metadata improves the performance on standard metrics, it favors self-citations which are less useful in a citation recommendation setup. We release an online portal for citation recommendation based on our method, (URL: http://bit.ly/citeDemo) and a new dataset OpenCorpus of 7 million research articles to facilitate future research on this task.

203. Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse [PDF] 返回目录
NAACL 2018. Long Papers
Xingshan Zeng, Jing Li, Lu Wang, Nicholas Beauchamp, Sarah Shugars, Kam-Fai Wong
Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation. While much prior work has focused on post-level recommendation, we exploit both the conversational context, and user content and behavior preferences. We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics. Experimental results on two Twitter datasets demonstrate that our system outperforms methods that only model content without considering discourse.

204. Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation [PDF] 返回目录
NAACL 2018. Long Papers
Mingmin Jin, Xin Luo, Huiling Zhu, Hankz Hankui Zhuo
With the rise of e-commerce, people are accustomed to writing their reviews after receiving the goods. These comments are so important that a bad review can have a direct impact on others buying. Besides, the abundant information within user reviews is very useful for extracting user preferences and item properties. In this paper, we investigate the approach to effectively utilize review information for recommender systems. The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding. In the experiments on popular review dataset Amazon , our LTMF model outperforms previous proposed HFT model and ConvMF model in rating prediction. Furthermore, LTMF shows the better ability on making topic clustering than traditional topic model based method, which implies integrating the information from deep learning and topic modeling is a meaningful approach to make a better understanding of reviews.

205. Mining Evidences for Concept Stock Recommendation [PDF] 返回目录
NAACL 2018. Long Papers
Qi Liu, Yue Zhang
We investigate the task of mining relevant stocks given a topic of concern on emerging capital markets, for which there is lack of structural understanding. Deep learning is leveraged to mine evidences from large scale textual data, which contain valuable market information. In particular, distributed word similarities trained over large scale raw texts are taken as a basis of relevance measuring, and deep reinforcement learning is leveraged to learn a strategy of topic expansion, given a small amount of manually labeled data from financial analysts. Results on two Chinese stock market datasets show that our method outperforms a strong baseline using information retrieval techniques.

206. Document-based Recommender System for Job Postings using Dense Representations [PDF] 返回目录
NAACL 2018. Industry Papers
Ahmed Elsafty, Martin Riedl, Chris Biemann
Job boards and professional social networks heavily use recommender systems in order to better support users in exploring job advertisements. Detecting the similarity between job advertisements is important for job recommendation systems as it allows, for example, the application of item-to-item based recommendations. In this work, we research the usage of dense vector representations to enhance a large-scale job recommendation system and to rank German job advertisements regarding their similarity. We follow a two-folded evaluation scheme: (1) we exploit historic user interactions to automatically create a dataset of similar jobs that enables an offline evaluation. (2) In addition, we conduct an online A/B test and evaluate the best performing method on our platform reaching more than 1 million users. We achieve the best results by combining job titles with full-text job descriptions. In particular, this method builds dense document representation using words of the titles to weigh the importance of words of the full-text description. In the online evaluation, this approach allows us to increase the click-through rate on job recommendations for active users by 8.0%.

207. A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers [PDF] 返回目录
NeurIPS 2018.
Omer Ben-Porat, Moshe Tennenholtz
We introduce a game-theoretic approach to the study of recommendation systems with strategic content providers. Such systems should be fair and stable. Showing that traditional approaches fail to satisfy these requirements, we propose the Shapley mediator. We show that the Shapley mediator satisfies the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties.

208. Fighting Boredom in Recommender Systems with Linear Reinforcement Learning [PDF] 返回目录
NeurIPS 2018.
Romain WARLOP, Alessandro Lazaric, Jérémie Mary
A common assumption in recommender systems (RS) is the existence of a best fixed recommendation strategy. Such strategy may be simple and work at the item level (e.g., in multi-armed bandit it is assumed one best fixed arm/item exists) or implement more sophisticated RS (e.g., the objective of A/B testing is to find thebest fixed RS and execute it thereafter). We argue that this assumption is rarely verified in practice, as the recommendation process itself may impact the user’spreferences. For instance, a user may get bored by a strategy, while she may gain interest again, if enough time passed since the last time that strategy was used. Inthis case, a better approach consists in alternating different solutions at the right frequency to fully exploit their potential. In this paper, we first cast the problem asa Markov decision process, where the rewards are a linear function of the recent history of actions, and we show that a policy considering the long-term influenceof the recommendations may outperform both fixed-action and contextual greedy policies. We then introduce an extension of the UCRL algorithm ( L IN UCRL ) toeffectively balance exploration and exploitation in an unknown environment, and we derive a regret bound that is independent of the number of states. Finally,we empirically validate the model assumptions and the algorithm in a number of realistic scenarios.

209. Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity [PDF] 返回目录
NeurIPS 2018.
Laming Chen, Guoxin Zhang, Eric Zhou
The determinantal point process (DPP) is an elegant probabilistic model of repulsion with applications in various machine learning tasks including summarization and search. However, the maximum a posteriori (MAP) inference for DPP which plays an important role in many applications is NP-hard, and even the popular greedy algorithm can still be too computationally expensive to be used in large-scale real-time scenarios. To overcome the computational challenge, in this paper, we propose a novel algorithm to greatly accelerate the greedy MAP inference for DPP. In addition, our algorithm also adapts to scenarios where the repulsion is only required among nearby few items in the result sequence. We apply the proposed algorithm to generate relevant and diverse recommendations. Experimental results show that our proposed algorithm is significantly faster than state-of-the-art competitors, and provides a better relevance-diversity trade-off on several public datasets, which is also confirmed in an online A/B test.

210. Modeling Dynamic Missingness of Implicit Feedback for Recommendation [PDF] 返回目录
NeurIPS 2018.
Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang
Implicit feedback is widely used in collaborative filtering methods for recommendation. It is well known that implicit feedback contains a large number of values that are \emph{missing not at random} (MNAR); and the missing data is a mixture of negative and unknown feedback, making it difficult to learn user's negative preferences. Recent studies modeled \emph{exposure}, a latent missingness variable which indicates whether an item is missing to a user, to give each missing entry a confidence of being negative feedback.However, these studies use static models and ignore the information in temporal dependencies among items, which seems to be a essential underlying factor to subsequent missingness. To model and exploit the dynamics of missingness, we propose a latent variable named ``\emph{user intent}'' to govern the temporal changes of item missingness, and a hidden Markov model to represent such a process. The resulting framework captures the dynamic item missingness and incorporate it into matrix factorization (MF) for recommendation. We also explore two types of constraints to achieve a more compact and interpretable representation of \emph{user intents}. Experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommender systems.

211. Online Reciprocal Recommendation with Theoretical Performance Guarantees [PDF] 返回目录
NeurIPS 2018.
Fabio Vitale, Nikos Parotsidis, Claudio Gentile
A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences of both users. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to those achieved by a clairvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines.

212. Towards Deep Conversational Recommendations [PDF] 返回目录
NeurIPS 2018.
Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations.To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

213. Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes [PDF] 返回目录
AAAI 2017. Artificial Intelligence and the Web
Hideaki Kim, Tomoharu Iwata, Yasuhiro Fujiwara, Naonori Ueda
Everything has its time, which is also true in the point-of-interest (POI) recommendation task. A truly intelligent recommender system, even if you don't visit any sites or remain silent, should draw hints of your next destination from the ``silence", and revise its recommendations as needed. In this paper, we construct a well-timed POI recommender system that updates its recommendations in accordance with the silence, the temporal period in which no visits are made. To achieve this, we propose a novel probabilistic model to predict the joint probabilities of the user visiting POIs and their time-points, by using the admixture or mixed-membership structure to extend marked point processes. With the admixture structure, the proposed model obtains a low dimensional representation for each user, leading to robust recommendation against sparse observations. We also develop an efficient and easy-to-implement estimation algorithm for the proposed model based on collapsed Gibbs and slice sampling. We apply the proposed model to synthetic and real-world check-in data, and show that it performs well in the well-timed recommendation task.

214. Exploiting both Vertical and Horizontal Dimensions of Feature Hierarchy for Effective Recommendation [PDF] 返回目录
AAAI 2017. Artificial Intelligence and the Web
Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon
Feature hierarchy (FH) has proven to be effective to improve recommendation accuracy. Prior work mainly focuses on the influence of vertically affiliated features (i.e. child-parent) on user-item interactions. The relationships of horizontally organized features (i.e. siblings and cousins) in the hierarchy, however, has only been little investigated. We show in real-world datasets that feature relationships in horizontal dimension can help explain and further model user-item interactions. To fully exploit FH, we propose a unified recommendation framework that seamlessly incorporates both vertical and horizontal dimensions for effective recommendation. Our model further considers two types of semantically rich feature relationships in horizontal dimension, i.e. complementary and alternative relationships. Extensive validation on four real-world datasets demonstrates the superiority of our approach against the state of the art. An additional benefit of our model is to provide better interpretations of the generated recommendations.

215. CLARE: A Joint Approach to Label Classification and Tag Recommendation [PDF] 返回目录
AAAI 2017. Artificial Intelligence and the Web
Yilin Wang, Suhang Wang, Jiliang Tang, Guo-Jun Qi, Huan Liu, Baoxin Li
Data classification and tag recommendation are both important and challenging tasks in social media. These two tasks are often considered independently and most efforts have been made to tackle them separately. However, labels in data classification and tags in tag recommendation are inherently related. For example, a Youtube video annotated with NCAA, stadium, pac12 is likely to be labeled as football, while a video/image with the class label of coast is likely to be tagged with beach, sea, water and sand. The existence of relations between labels and tags motivates us to jointly perform classification and tag recommendation for social media data in this paper. In particular, we provide a principled way to capture the relations between labels and tags, and propose a novel framework CLARE, which fuses data CLAssification and tag REcommendation into a coherent model. With experiments on three social media datasets, we demonstrate that the proposed framework CLARE achieves superior performance on both tasks compared to the state-of-the-art methods.

216. Examples-Rules Guided Deep Neural Network for Makeup Recommendation [PDF] 返回目录
AAAI 2017. Human-Aware Artificial Intelligence
Taleb Alashkar, Songyao Jiang, Shuyang Wang, Yun Fu
In this paper, we consider a fully automatic makeup recommendation system and propose a novel examples-rules guided deep neural network approach. The framework consists of three stages. First, makeup-related facial traits are classified into structured coding. Second, these facial traits are fed in- to examples-rules guided deep neural recommendation model which makes use of the pairwise of Before-After images and the makeup artist knowledge jointly. Finally, to visualize the recommended makeup style, an automatic makeup synthesis system is developed as well. To this end, a new Before-After facial makeup database is collected and labeled manually, and the knowledge of makeup artist is modeled by knowledge base system. The performance of this framework is evaluated through extensive experimental analyses. The experiments validate the automatic facial traits classification, the recommendation effectiveness in statistical and perceptual ways and the makeup synthesis accuracy which outperforms the state of the art methods by large margin. It is also worthy to note that the proposed framework is a pioneering fully automatic makeup recommendation systems to our best knowledge.

217. A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems [PDF] 返回目录
AAAI 2017. Machine Learning Applications
Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, Fangxi Zhang
Collaborative filtering (CF) is a widely used approach in recommender systems to solve many real-world problems. Traditional CF-based methods employ the user-item matrix which encodes the individual preferences of users for items for learning to make recommendation. In real applications, the rating matrix is usually very sparse, causing CF-based methods to degrade significantly in recommendation performance. In this case, some improved CF methods utilize the increasing amount of side information to address the data sparsity problem as well as the cold start problem. However, the learned latent factors may not be effective due to the sparse nature of the user-item matrix and the side information. To address this problem, we utilize advances of learning effective representations in deep learning, and propose a hybrid model which jointly performs deep users and items’ latent factors learning from side information and collaborative filtering from the rating matrix. Extensive experimental results on three real-world datasets show that our hybrid model outperforms other methods in effectively utilizing side information and achieves performance improvement.

218. Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation [PDF] 返回目录
AAAI 2017. Machine Learning Applications
Li Gao, Jia Wu, Chuan Zhou, Yue Hu
In many time-aware item recommender systems, modeling the accurate evolution of both user profiles and the contents of items over time is essential. However, most existing methods focus on learning users' dynamic interests, where the contents of items are assumed to be stable over time. They thus fail to capture the dynamic changes in the item's contents. In this paper, we present a novel method CDUE for time-aware item recommendation, which captures the evolution of both user's interests and item's contents information via topic dynamics. Specifically, we propose a dynamic sparse topic model to track the evolution of topics for changes in items' contents over time and adapt a vector autoregressive model to profile users' dynamic interests. The item's topics and user's interests and their evolutions are learned collaboratively and simultaneously into a unified learning framework. Experimental results on two real-world data sets demonstrate the quality and effectiveness of the proposed method and show that our method can be used to make better future recommendations.

219. ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems [PDF] 返回目录
AAAI 2017. Machine Learning Applications
Dongsheng Li, Chao Chen, Qin Lv, Li Shang, Stephen M. Chu, Hongyuan Zha
Matrix approximation (MA) is one of the most popular techniques in today's recommender systems. In most MA-based recommender systems, the problem of risk minimization should be defined, and how to achieve minimum expected risk in model learning is one of the most critical problems to recommendation accuracy. This paper addresses the expected risk minimization problem, in which expected risk can be bounded by the sum of optimization error and generalization error. Based on the uniform stability theory, we propose an expected risk minimized matrix approximation method (ERMMA), which is designed to achieve better tradeoff between optimization error and generalization error in order to reduce the expected risk of the learned MA models. Theoretical analysis shows that ERMMA can achieve lower expected risk bound than existing MA methods. Experimental results on the MovieLens and Netflix datasets demonstrate that ERMMA outperforms six state-of-the-art MA-based recommendation methods in both rating prediction problem and item ranking problem.

220. Finding Cut from the Same Cloth: Cross Network Link Recommendation via Joint Matrix Factorization [PDF] 返回目录
AAAI 2017. Machine Learning Applications
Arun Reddy Nelakurthi, Jingrui He
With the emergence of online forums associated with major diseases, such as diabetes mellitus, many patients are increasingly dependent on such disease-specific social networks to gain access to additional resources. Among these patients, it is common for them to stick to one disease-specific social network, although their desired resources might be spread over multiple social networks, such as patients with similar questions and concerns. Motivated by this application, in this paper, we focus on cross network link recommendation, which aims to identify similar users across multiple heterogeneous social networks. The problem setting is different from existing work on cross network link prediction, which either tries to link accounts of the same user from different social networks, or aims to match users with complementary expertise or interest. To approach the problem of cross network link recommendation, we propose to jointly decompose the user-keyword matrices from multiple social networks, while requiring them to share the same topics and user group-topic association matrices. This constraint comes from the fact that social networks dedicated to the same disease tend to share the same topics as well as the interests of users groups in certain topics. Based on this intuition, we construct a generic optimization framework, provide four instantiations and an iterative optimization algorithm with performance analysis. In the experiments, we demonstrate the superiority of the proposed algorithm over state-of-the-art techniques on various real-world data sets.

221. Low-Rank Linear Cold-Start Recommendation from Social Data [PDF] 返回目录
AAAI 2017. Machine Learning Applications
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie, Darius Braziunas
The cold-start problem involves recommendation of content to new users of a system, for whom there is no historical preference information available. This proves a challenge for collaborative filtering algorithms that inherently rely on such information. Recent work has shown that social metadata, such as users' friend groups and page likes, can strongly mitigate the problem. However, such approaches either lack an interpretation as optimising some principled objective, involve iterative non-convex optimisation with limited scalability, or require tuning several hyperparameters. In this paper, we first show how three popular cold-start models are special cases of a linear content-based model, with implicit constraints on the weights. Leveraging this insight, we propose Loco, a new model for cold-start recommendation based on three ingredients: (a) linear regression to learn an optimal weighting of social signals for preferences, (b) a low-rank parametrisation of the weights to overcome the high dimensionality common in social data, and (c) scalable learning of such low-rank weights using randomised SVD. Experiments on four real-world datasets show that Loco yields significant improvements over state-of-the-art cold-start recommenders that exploit high-dimensional social network metadata.

222. Factorization Bandits for Interactive Recommendation [PDF] 返回目录
AAAI 2017. Machine Learning Methods
Huazheng Wang, Qingyun Wu, Hongning Wang
We perform online interactive recommendation via a factorization-based bandit algorithm. Low-rank matrix completion is performed over an incrementally constructed user-item preference matrix, where an upper confidence bound based item selection strategy is developed to balance the exploit/explore trade-off during online learning. Observable contextual features and dependency among users (e.g., social influence) are leveraged to improve the algorithm's convergence rate and help conquer cold-start in recommendation. A high probability sublinear upper regret bound is proved for the developed algorithm, where considerable regret reduction is achieved on both user and item sides. Extensive experimentations on both simulations and large-scale real-world datasets confirmed the advantages of the proposed algorithm compared with several state-of-the-art factorization-based and bandit-based collaborative filtering methods.

223. Efficient Ordered Combinatorial Semi-Bandits for Whole-Page Recommendation [PDF] 返回目录
AAAI 2017. Machine Learning Methods
Yingfei Wang, Hua Ouyang, Chu Wang, Jianhui Chen, Tsvetan Asamov, Yi Chang
Multi-Armed Bandit (MAB) framework has been successfully applied in many web applications. However, many complex real-world applications that involve multiple content recommendations cannot fit into the traditional MAB setting. To address this issue, we consider an ordered combinatorial semi-bandit problem where the learner recommends S actions from a base set of K actions, and displays the results in S (out of M) different positions. The aim is to maximize the cumulative reward with respect to the best possible subset and positions in hindsight. By the adaptation of a minimum-cost maximum-flow network, a practical algorithm based on Thompson sampling is derived for the (contextual) combinatorial problem, thus resolving the problem of computational intractability.With its potential to work with whole-page recommendation and any probabilistic models, to illustrate the effectiveness of our method, we focus on Gaussian process optimization and a contextual setting where click-through rate is predicted using logistic regression. We demonstrate the algorithms’ performance on synthetic Gaussian process problems and on large-scale news article recommendation datasets from Yahoo! Front Page Today Module.

224. SenseRun: Real-Time Running Routes Recommendation towards Providing Pleasant Running Experiences [PDF] 返回目录
AAAI 2017. Demonstrations
Jiayu Long, Jia Jia, Han Xu
In this demo, we develop a mobile running application, SenseRun, to involve landscape experiences for routes recommendation. We firstly define landscape experiences, perceived enjoyment from landscape as motivators for running, by public natural area and traffic density. Based on landscape experiences, we categorize locations into 3 types (natural, leisure, traffic space) and set them with different basic weight. Real-time context factors (weather, season and hour of the day) are involved to adjust the weight. We propose a multi-attributes method to recommend routes with weight based on MVT (The Marginal Value Theorem) k-shortest-paths algorithm. We also use a landscape-awareness sounds algorithm as supplementary of landscape experiences. Experimental results improve that SenseRun can enhance running experiences and is helpful to promote regular physical activities.

225. Opinion Recommendation Using A Neural Model [PDF] 返回目录
EMNLP 2017.
Zhongqing Wang, Yue Zhang
We present opinion recommendation, a novel task of jointly generating a review with a rating score that a certain user would give to a certain product which is unreviewed by the user, given existing reviews to the product by other users, and the reviews that the user has given to other products. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning. We use a single neural network to model users and products, generating customised product representations using a deep memory network, from which customised ratings and reviews are constructed jointly. Results show that our opinion recommendation system gives ratings that are closer to real user ratings on Yelp.com data compared with Yelp’s own ratings. our methods give better results compared to several pipelines baselines.

226. Assessing Objective Recommendation Quality through Political Forecasting [PDF] 返回目录
EMNLP 2017.
H. Andrew Schwartz, Masoud Rouhizadeh, Michael Bishop, Philip Tetlock, Barbara Mellers, Lyle Ungar
Recommendations are often rated for their subjective quality, but few researchers have studied comment quality in terms of objective utility. We explore recommendation quality assessment with respect to both subjective (i.e. users’ ratings) and objective (i.e., did it influence? did it improve decisions?) metrics in a massive online geopolitical forecasting system, ultimately comparing linguistic characteristics of each quality metric. Using a variety of features, we predict all types of quality with better accuracy than the simple yet strong baseline of comment length. Looking at the most predictive content illustrates rater biases; for example, forecasters are subjectively biased in favor of comments mentioning business transactions or dealings as well as material things, even though such comments do not indeed prove any more useful objectively. Additionally, more complex sentence constructions, as evidenced by subordinate conjunctions, are characteristic of comments leading to objective improvements in forecasting.

227. Proposing a Highly Accurate Hybrid Component-Based Factorised Preference Model in Recommender Systems [PDF] 返回目录
IJCAI 2017.
Farhad Zafari, Rasoul Rahmani, Irene Moser
Recommender systems play an important role in today's electronic markets due to the large benefits they bring by helping businesses understand their customers' needs and preferences. The major preference components modelled by current recommender systems include user and item biases, feature value preferences, conditional dependencies, temporal preference drifts, and social influence on preferences. In this paper, we introduce a new hybrid latent factor model that achieves great accuracy by integrating all these preference components in a unified model efficiently. The proposed model employs gradient descent to optimise the model parameters, and an evolutionary algorithm to optimise the hyper-parameters and gradient descent learning rates. Using two popular datasets, we investigate the interaction effects of the preference components with each other.We conclude that depending on the dataset, different interactions exist between the preference components. Therefore, understanding these interaction effects is crucial in designing an accurate preference model in every preference dataset and domain.Our results show that on both datasets, different combinations of components result in different accuracies of recommendation, suggesting that some parts of the model interact strongly. Moreover, these effects are highly dataset-dependent, suggesting the need for exploring these effects before choosing the appropriate combination of components.

228. SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation [PDF] 返回目录
IJCAI 2017.
Chenwei Cai, Ruining He, Julian J. McAuley
Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems. These issues can partly be dealt with by making predictions not in isolation, but by leveraging information from related events; such information could include signals from social relationships or from the sequence of recent activities. Both types of additional information can be used to improve the performance of state-of-the-art matrix factorization-based techniques. In this paper, we propose new methods to combine both social and sequential information simultaneously, in order to further improve recommendation performance. We show these techniques to be particularly effective when dealing with sparsity and cold-start issues in several large, real-world datasets.

229. Improved Bounded Matrix Completion for Large-Scale Recommender Systems [PDF] 返回目录
IJCAI 2017.
Huang Fang, Zhen Zhang, Yiqun Shao, Cho-Jui Hsieh
Matrix completion is a widely used technique for personalized recommender system. In this paper, we focus on the idea of Bounded Matrix Completion (BMC) which imposes bounded constraint into the original matrix completion problem. It has been shown that BMC works well for several real world datasets, and an efficient coordinate descent solver called BMA has been proposed in~\cite{bma}. However, we observe that the BMA algorithm sometimes fails to converge to a stationary point, resulting in a relatively poor accuracy in those cases. To overcome this issue, we propose our new approach for solving BMC under the ADMM framework. The proposed algorithm is gauranteed to converge to stationary points. Experimental results on real world datasets show that our algorithm can reach a lower objective value, obtain a higher predict accuracy rate and have better scalability compared with BMA. We also present that our method outperforms the state-of-art standard matrix factorization in most cases.

230. Understanding Users' Budgets for Recommendation with Hierarchical Poisson Factorization [PDF] 返回目录
IJCAI 2017.
Yunhui Guo, Congfu Xu, Hanzhang Song, Xin Wang
People consume and rate products in online shopping websites. The historical purchases of customers reflect their personal consumption habits and indicate their future shopping behaviors. Traditional preference-based recommender systems try to provide recommendations by analyzing users' feedback such as ratings and clicks. But unfortunately, most of the existing recommendation algorithms ignore the budget of the users. So they cannot avoid recommending users with products that will exceed their budgets. And they also cannot understand how the users will assign their budgets to different products. In this paper, we develop a generative model named collaborative budget-aware Poisson factorization (CBPF) to connect users' ratings and budgets. The CBPF model is intuitive and highly interpretable. We compare the proposed model with several state-of-the-art budget-unaware recommendation methods on several real-world datasets. The results show the advantage of uncovering users' budgets for recommendation.

231. Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking [PDF] 返回目录
IJCAI 2017.
Jing He, Xin Li, Lejian Liao
Next Point-of-interest (POI) recommendation has become an important task for location-based social networks (LBSNs). However, previous efforts suffer from the high computational complexity and the transition pattern between POIs has not been well studied. In this paper, we propose a two-fold approach for next POI recommendation. First, the preferred next category is predicted by using a third-rank tensor optimized by a Listwise Bayesian Personalized Ranking (LBPR) approach. Specifically we introduce two functions, namely Plackett-Luce model and cross entropy, to generate the likelihood of ranking list for posterior computation. Then POI candidates filtered by the predicated category are ranked based on the spatial influence and category ranking influence. Extensive experiments on two real-world datasets demonstrate the significant improvements of our methods over several state-of-the-art methods.

232. Diversifying Personalized Recommendation with User-session Context [PDF] 返回目录
IJCAI 2017.
Liang Hu, Longbing Cao, Shoujin Wang, Guandong Xu, Jian Cao, Zhiping Gu
Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.

233. Mention Recommendation for Twitter with End-to-end Memory Network [PDF] 返回目录
IJCAI 2017.
Haoran Huang, Qi Zhang, Xuanjing Huang
In this study, we investigated the problem of recommending usernames when people attempt to use the ``@'' sign to mention other people in twitter-like social media. With the extremely rapid development of social networking services, this problem has received considerable attention in recent years. Previous methods have studied the problem from different aspects. Because most of Twitter-like microblogging services limit the length of posts, statistical learning methods may be affected by the problems of word sparseness and synonyms. Although recent progress in neural word embedding methods have advanced the state-of-the-art in many natural language processing tasks, the benefits of word embedding have not been taken into consideration for this problem. In this work, we proposed a novel end-to-end memory network architecture to perform this task. We incorporated the interests of users with external memory. A hierarchical attention mechanism was also applied to better consider the interests of users. The experimental results on a dataset we collected from Twitter demonstrated that the proposed method could outperform state-of-the-art approaches.

234. Basket-Sensitive Personalized Item Recommendation [PDF] 返回目录
IJCAI 2017.
Duc-Trong Le, Hady Wirawan Lauw, Yuan Fang
Personalized item recommendation is useful in narrowing down the list of options provided to a user. In this paper, we address the problem scenario where the user is currently holding a basket of items, and the task is to recommend an item to be added to the basket. Here, we assume that items currently in a basket share some association based on an underlying latent need, e.g., ingredients to prepare some dish, spare parts of some device. Thus, it is important that a recommended item is relevant not only to the user, but also to the existing items in the basket. Towards this goal, we propose two approaches. First, we explore a factorization-based model called BFM that incorporates various types of associations involving the user, the target item to be recommended, and the items currently in the basket. Second, based on our observation that various recommendations towards constructing the same basket should have similar likelihoods, we propose another model called CBFM that further incorporates basket-level constraints. Experiments on three real-life datasets from different domains empirically validate these models against baselines based on matrix factorization and association rules.

235. Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach [PDF] 返回目录
IJCAI 2017.
Huayu Li, Yong Ge, Defu Lian, Hao Liu
Point-of-Interest (POI) recommendation has been an important service on location-based social networks. However, it is very challenging to generate accurate recommendations due to the complex nature of user's interest in POI and the data sparseness. In this paper, we propose a novel unified approach that could effectively learn fine-grained and interpretable user's interest, and adaptively model the missing data. Specifically, a user's general interest in POI is modeled as a mixture of her intrinsic and extrinsic interests, upon which we formulate the ranking constraints in our unified recommendation approach. Furthermore, a self-adaptive location-oriented method is proposed to capture the inherent property of missing data, which is formulated as squared error based loss in our unified optimization objective. Extensive experiments on real-world datasets demonstrate the effectiveness and advantage of our approach.

236. Learning User Dependencies for Recommendation [PDF] 返回目录
IJCAI 2017.
Yong Liu, Peilin Zhao, Xin Liu, Min Wu, Lixin Duan, Xiaoli Li
Social recommender systems exploit users' social relationships to improve recommendation accuracy. Intuitively, a user tends to trust different people regarding with different scenarios. Therefore, one main challenge of social recommendation is to exploit the most appropriate dependencies between users for a given recommendation task. Previous social recommendation methods are usually developed based on pre-defined user dependencies. Thus, they may not be optimal for a specific recommendation task. In this paper, we propose a novel recommendation method, named probabilistic relational matrix factorization (PRMF), which can automatically learn the dependencies between users to improve recommendation accuracy. In PRMF, users' latent features are assumed to follow a matrix variate normal (MVN) distribution. Both positive and negative user dependencies can be modeled by the row precision matrix of the MVN distribution. Moreover, we also propose an alternating optimization algorithm to solve the optimization problem of PRMF. Extensive experiments on four real datasets have been performed to demonstrate the effectiveness of the proposed PRMF model.

237. Cross-Domain Recommendation: An Embedding and Mapping Approach [PDF] 返回目录
IJCAI 2017.
Tong Man, Huawei Shen, Xiaolong Jin, Xueqi Cheng
Data sparsity is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation, i.e., leveraging feedbacks or ratings from multiple domains to improve recommendation performance in a collective manner. In this paper, we propose an Embedding and Mapping framework for Cross-Domain Recommendation, called EMCDR. The proposed EMCDR framework distinguishes itself from existing cross-domain recommendation models in two aspects. First, a multi-layer perceptron is used to capture the nonlinear mapping function across domains, which offers high flexibility for learning domain-specific features of entities in each domain. Second, only the entities with sufficient data are used to learn the mapping function, guaranteeing its robustness to noise caused by data sparsity in single domain. Extensive experiments on two cross-domain recommendation scenarios demonstrate that EMCDR significantly outperforms state-of-the-art cross-domain recommendation methods.

238. Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness [PDF] 返回目录
IJCAI 2017.
Kaisong Song, Wei Gao, Shi Feng, Daling Wang, Kam-Fai Wong, Chengqi Zhang
Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of reviews, user preferences and product characteristics by jointly optimizing two components, a user-specific LFM and a product-specific LFM, each of which decomposes text into a specific low-dimension representation. Furthermore, we address the cold-start issue by developing a novel Pairwise Rating Comparison strategy (PRC), which utilizes the difference between ratings on common user/product as supplementary information to calibrate parameter estimation. Experiments conducted on IMDB and Yelp datasets validate the advantage of our approach over state-of-the-art baseline methods.

239. MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation [PDF] 返回目录
IJCAI 2017.
Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Yu Chen, Chi Xu
Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.

240. Tag-Aware Personalized Recommendation Using a Hybrid Deep Model [PDF] 返回目录
IJCAI 2017.
Zhenghua Xu, Thomas Lukasiewicz, Cheng Chen, Yishu Miao, Xiangwu Meng
Recently, many efforts have been put into tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). To ensure the scalability in practice, we further propose to improve this model's training efficiency by using hybrid deep learning and negative sampling. Experimental results show that our approach can significantly outperform the state-of-the-art baselines in tag-aware personalized recommendation (3.8 times better than the best baseline), and that using hybrid deep learning and negative sampling can dramatically enhance the model's training efficiency (hundreds of times quicker), while maintaining similar (and sometimes even better) training quality and recommendation performance.

241. Deep Matrix Factorization Models for Recommender Systems [PDF] 返回目录
IJCAI 2017.
Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, Jiajun Chen
Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the basic idea to predict a personalized ranking over a set of items for an individual user with the similarities among users and items. In this paper, we propose a novel matrix factorization model with neural network architecture. Firstly, we construct a user-item matrix with explicit ratings and non-preference implicit feedback. With this matrix as the input, we present a deep structure learning architecture to learn a common low dimensional space for the representations of users and items. Secondly, we design a new loss function based on binary cross entropy, in which we consider both explicit ratings and implicit feedback for a better optimization. The experimental results show the effectiveness of both our proposed model and the loss function. On several benchmark datasets, our model outperformed other state-of-the-art methods. We also conduct extensive experiments to evaluate the performance within different experimental settings.

242. FolkPopularityRank: Tag Recommendation for Enhancing Social Popularity using Text Tags in Content Sharing Services [PDF] 返回目录
IJCAI 2017.
Toshihiko Yamasaki, Jiani Hu, Shumpei Sano, Kiyoharu Aizawa
In this study, we address two emerging yet challenging problems in social media: (1) scoring the text tags in terms of the influence to the numbers of views, comments, and favorite ratings of images and videos on content sharing services, and (2) recommending additional tags to increase such popularity-related numbers.For these purposes, we present the FolkPopularityRank algorithm, which can score text tags based on their ability to influence the popularity-related numbers. The FolkPopularityRank algorithm is inspired by the PageRank and FolkRank algorithms but the scores of the tags are calculated not only by the co-occurrence of the tags but also by considering the popularity-related numbers of the content.To the best of our knowledge, this is the first attempt to recommending tags that can enhance popularity attributes of social media.We conducted extensive experiments with about 1,000 images.We uploaded the photos with the recommended tags along with the original tags to Flickr as a real test, and obtained very promising results.

243. Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network [PDF] 返回目录
IJCAI 2017.
Qi Zhang, Jiawen Wang, Haoran Huang, Xuanjing Huang, Yeyun Gong
In microblogging services, authors can use hashtags to mark keywords or topics. Many live social media applications (e.g., microblog retrieval, classification) can gain great benefits from these manually labeled tags. However, only a small portion of microblogs contain hashtags inputed by users. Moreover, many microblog posts contain not only textual content but also images. These visual resources also provide valuable information that may not be included in the textual content. So that it can also help to recommend hashtags more accurately. Motivated by the successful use of the attention mechanism, we propose a co-attention network incorporating textual and visual information to recommend hashtags for multimodal tweets. Experimental result on the data collected from Twitter demonstrated that the proposed method can achieve better performance than state-of-the-art methods using textual information only.

244. Learning Discriminative Recommendation Systems with Side Information [PDF] 返回目录
IJCAI 2017.
Feipeng Zhao, Yuhong Guo
Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation systems. This joint model aggregates observed user-item recommendation activities to produce the missing user-item recommendation scores while simultaneously training a linear regression model to predict the user-item recommendation scores from auxiliary item features. We evaluate the proposed approach on a number of recommendation datasets. The experimental results show that the proposed joint model is very effective for producing top-N recommendation systems.

245. Exploiting Music Play Sequence for Music Recommendation [PDF] 返回目录
IJCAI 2017.
Zhiyong Cheng, Jialie Shen, Lei Zhu, Mohan S. Kankanhalli, Liqiang Nie
Users leave digital footprints when interacting with various music streaming services. Music play sequence, which contains rich information about personal music preference and song similarity, has been largely ignored in previous music recommender systems. In this paper, we explore the effects of music play sequence on developing effective personalized music recommender systems. Towards the goal, we propose to use word embedding techniques in music play sequences to estimate the similarity between songs. The learned similarity is then embedded into matrix factorization to boost the latent feature learning and discovery. Furthermore, the proposed method only considers the k-nearest songs (e.g., k = 5) in the learning process and thus avoids the increase of time complexity. Experimental results on two public datasets demonstrate that our methods could significantly improve the performance of both rating prediction and top-n recommendation tasks.

246. User-Based Opinion-based Recommendation [PDF] 返回目录
IJCAI 2017.
Ruihai Dong, Barry Smyth
User-generated reviews are a plentiful source of user opinions and interests and can play an important role in a range of artificial intelligence contexts, particularly when it comes to recommender systems. In this paper, we describe how natural language processing and opinion mining techniques can be used to automatically mine useful recommendation knowledge from user generated reviews and how this information can be used by recommender systems in a number of classical settings.

247. Constructive Recommendation [PDF] 返回目录
IJCAI 2017.
Paolo Dragone
Constructive recommendation is the task of recommending object “configurations”, i.e. objects that can be assembled from their components on the basis of the user preferences. Examples include: PC configurations, recipes, travel plans, layouts, and other structured objects. Recommended objects are created by maximizing a learned utility function over an exponentially (or even infinitely) large combinatorial space of configurations. The utility function is learned through preference elicitation, an interactive process for collecting user feedback about recommended objects. Constructive recommendation brings up a wide range of possible applications as well as many untackled research problems, ranging from the unprecedented complexity of the inference problem to the nontrivial choice of the type of user interaction.

248. Become Popular in SNS: Tag Recommendation using FolkPopularityRank to Enhance Social Popularity [PDF] 返回目录
IJCAI 2017.
Toshihiko Yamasaki, Yiwei Zhang, Jiani Hu, Shumpei Sano, Kiyoharu Aizawa
In this demo, we address two emerging yet challenging problems in social media: (1) scoring the text tags in terms of the influence to the numbers of views, comments, and favorite ratings of images and videos on content sharing services, and (2) recommending additional tags to increase such popularity-related numbers.For these purposes, we present a demo using our FolkPopularityRank (FP-Rank) algorithm, which can score and recommend text tags based on their ability to influence the popularity-related numbers. Our experiments using 1,000 photos showed that we can achieve 1.6 times more views than the original tag sets in Flickr just by adding tags recommended by FP-Rank.

249. Scalable Demand-Aware Recommendation [PDF] 返回目录
NeurIPS 2017.
Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li
Recommendation for e-commerce with a mix of durable and nondurable goods has characteristics that distinguish it from the well-studied media recommendation problem. The demand for items is a combined effect of form utility and time utility, i.e., a product must both be intrinsically appealing to a consumer and the time must be right for purchase. In particular for durable goods, time utility is a function of inter-purchase duration within product category because consumers are unlikely to purchase two items in the same category in close temporal succession. Moreover, purchase data, in contrast to rating data, is implicit with non-purchases not necessarily indicating dislike. Together, these issues give rise to the positive-unlabeled demand-aware recommendation problem that we pose via joint low-rank tensor completion and product category inter-purchase duration vector estimation. We further relax this problem and propose a highly scalable alternating minimization approach with which we can solve problems with millions of users and millions of items in a single thread. We also show superior prediction accuracies on multiple real-world datasets.

250. Off-policy evaluation for slate recommendation [PDF] 返回目录
NeurIPS 2017.
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal, Miro Dudik, John Langford, Damien Jose, Imed Zitouni
This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context---a common scenario in web search, ads, and recommendation. We build on techniques from combinatorial bandits to introduce a new practical estimator that uses logged data to estimate a policy's performance. A thorough empirical evaluation on real-world data reveals that our estimator is accurate in a variety of settings, including as a subroutine in a learning-to-rank task, where it achieves competitive performance. We derive conditions under which our estimator is unbiased---these conditions are weaker than prior heuristics for slate evaluation---and experimentally demonstrate a smaller bias than parametric approaches, even when these conditions are violated. Finally, our theory and experiments also show exponential savings in the amount of required data compared with general unbiased estimators.

251. DropoutNet: Addressing Cold Start in Recommender Systems [PDF] 返回目录
NeurIPS 2017.
Maksims Volkovs, Guangwei Yu, Tomi Poutanen
Latent models have become the default choice for recommender systems due to their performance and scalability. However, research in this area has primarily focused on modeling user-item interactions, and few latent models have been developed for cold start. Deep learning has recently achieved remarkable success showing excellent results for diverse input types. Inspired by these results we propose a neural network based latent model called DropoutNet to address the cold start problem in recommender systems. Unlike existing approaches that incorporate additional content-based objective terms, we instead focus on the optimization and show that neural network models can be explicitly trained for cold start through dropout. Our model can be applied on top of any existing latent model effectively providing cold start capabilities, and full power of deep architectures. Empirically we demonstrate state-of-the-art accuracy on publicly available benchmarks. Code is available at https://github.com/layer6ai-labs/DropoutNet.

252. A Meta-Learning Perspective on Cold-Start Recommendations for Items [PDF] 返回目录
NeurIPS 2017.
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle
Matrix factorization (MF) is one of the most popular techniques for product recommendation, but is known to suffer from serious cold-start problems. Item cold-start problems are particularly acute in settings such as Tweet recommendation where new items arrive continuously. In this paper, we present a meta-learning strategy to address item cold-start when new items arrive continuously. We propose two deep neural network architectures that implement our meta-learning strategy. The first architecture learns a linear classifier whose weights are determined by the item history while the second architecture learns a neural network whose biases are instead adjusted. We evaluate our techniques on the real-world problem of Tweet recommendation. On production data at Twitter, we demonstrate that our proposed techniques significantly beat the MF baseline and also outperform production models for Tweet recommendation.

253. Capturing Semantic Correlation for Item Recommendation in Tagging Systems [PDF] 返回目录
AAAI 2016. Technical Papers: AI and the Web
Chaochao Chen, Xiaolin Zheng, Yan Wang, Fuxing Hong, Deren Chen
The popularity of tagging systems provides a great opportunity to improve the performance of item recommendation. Although existing approaches use topic modeling to mine the semantic information of items by grouping the tags labelled for items, they overlook an important property that tags link users and items as a bridge. Thus these methods cannot deal with the data sparsity without commonly rated items (DS-WO-CRI) problem, limiting their recommendation performance. Towards solving this challenging problem, we propose a novel tag and rating based collaborative filtering (CF) model for item recommendation, which first uses topic modeling to mine the semantic information of tags for each user and for each item respectively, and then incorporates the semantic information into matrix factorization to factorize rating information and to capture the bridging feature of tags and ratings between users and items.As a result, our model captures the semantic correlation between users and items, and is able to greatly improve recommendation performance, especially in DS-WO-CRI situations.Experiments conducted on two popular real-world datasets demonstrate that our proposed model significantly outperforms the conventional CF approach, the state-of-the-art social relation based CF approach, and the state-of-the-art topic modeling based CF approaches in terms of both precision and recall, and it is an effective approach to the DS-WO-CRI problem.

254. Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns [PDF] 返回目录
AAAI 2016. Technical Papers: AI and the Web
Jing He, Xin Li, Lejian Liao, Dandan Song, William K. Cheung
In this paper, we address the problem of personalized next Point-of-interest (POI) recommendation which has become an important and very challenging task in location-based social networks (LBSNs), but not well studied yet. With the conjecture that, under different contextual scenario, human exhibits distinct mobility patterns, we attempt here to jointly model the next POI recommendation under the influence of user's latent behavior pattern. We propose to adopt a third-rank tensor to model the successive check-in behaviors. By incorporating softmax function to fuse the personalized Markov chain with latent pattern, we furnish a Bayesian Personalized Ranking (BPR) approach and derive the optimization criterion accordingly. Expectation Maximization (EM) is then used to estimate the model parameters. Extensive experiments on two large-scale LBSNs datasets demonstrate the significant improvements of our model over several state-of-the-art methods.

255. Top-N Recommender System via Matrix Completion [PDF] 返回目录
AAAI 2016. Technical Papers: AI and the Web
Zhao Kang, Chong Peng, Qiang Cheng
Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.

256. Recommendation with Social Dimensions [PDF] 返回目录
AAAI 2016. Technical Papers: AI and the Web
Jiliang Tang, Suhang Wang, Xia Hu, Dawei Yin, Yingzhou Bi, Yi Chang, Huan Liu
The pervasive presence of social media greatly enriches online users' social activities, resulting in abundant social relations. Social relations provide an independent source for recommendation, bringing about new opportunities for recommender systems. Exploiting social relations to improve recommendation performance attracts a great amount of attention in recent years. Most existing social recommender systems treat social relations homogeneously and make use of direct connections (or strong dependency connections). However, connections in online social networks are intrinsically heterogeneous and are a composite of various relations. While connected users in online social networks form groups, and users in a group share similar interests, weak dependency connections are established among these users when they are not directly connected. In this paper, we investigate how to exploit the heterogeneity of social relations and weak dependency connections for recommendation. In particular, we employ social dimensions to simultaneously capture heterogeneity of social relations and weak dependency connections, and provide principled ways to model social dimensions, and propose a recommendation framework SoDimRec which incorporates heterogeneity of social relations and weak dependency connections based on social dimensions. Experimental results on real-world data sets demonstrate the effectiveness of the proposed framework. We conduct further experiments to understand the important role of social dimensions in the proposed framework.

257. STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation [PDF] 返回目录
AAAI 2016. Technical Papers: AI and the Web
Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R. Lyu, Irwin King
Successive point-of-interest (POI) recommendation in location-based social networks (LBSNs) becomes a significant task since it helps users to navigate a number of candidate POIs and provides the best POI recommendations based on users’ most recent check-in knowledge. However, all existing methods for successive POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences, but ignore an important fact that successive POI recommendation is a time-subtle recommendation task. In fact, even with the same previous check-in information, users would prefer different successive POIs at different time. To capture the impact of time on successive POI recommendation, in this paper, we propose a spatial-temporal latent ranking (STELLAR) method to explicitly model the interactions among user, POI, and time. In particular, the proposed STELLAR model is built upon a ranking-based pairwise tensor factorization framework with a fine-grained modeling of user-POI, POI-time, and POI-POI interactions for successive POI recommendation. Moreover, we propose a new interval-aware weight utility function to differentiate successive check-ins’ correlations, which breaks the time interval constraint in prior work. Evaluations on two real-world datasets demonstrate that the STELLAR model outperforms state-of-the-art successive POI recommendation model about 20% in Precision@5 and Recall@5.

258. CAPReS: Context Aware Persona Based Recommendation for Shoppers [PDF] 返回目录
AAAI 2016. Technical Papers: Heuristic Search and Optimization
Joydeep Banerjee, Gurulingesh Raravi, Manoj Gupta, Sindhu K. Ernala, Shruti Kunde, Koustuv Dasgupta
Nowadays, brick-and-mortar stores are finding it extremely difficult to retain their customers due to the ever increasing competition from the online stores. One of the key reasons for this is the lack of personalized shopping experience offered by the brick-and-mortar stores. This work considers the problem of persona based shopping recommendation for such stores to maximize the value for money of the shoppers. For this problem, it proposes a non-polynomial time-complexity optimal dynamic program and a polynomial time-complexity non-optimal heuristic, for making top-k recommendations by taking into account shopper persona and her time and budget constraints. In our empirical evaluations with a mix of real-world data and simulated data, the performance of the heuristic in terms of the persona based recommendations (quantified by similarity scores and items recommended) closely matched (differed by only 8% each with) that of the dynamic program and at the same time heuristic ran at least twice faster compared to the dynamic program.

259. Expressive Recommender Systems through Normalized Nonnegative Models [PDF] 返回目录
AAAI 2016. Technical Papers: Knowledge Representation and Reasoning
Cyril J. Stark
We introduce normalized nonnegative models (NNM) for explorative data analysis. NNMs are partial convexifications of models from probability theory. We demonstrate their value at the example of item recommendation. We show that NNM-based recommender systems satisfy three criteria that all recommender systems should ideally satisfy: high predictive power, computational tractability, and expressive representations of users and items. Expressive user and item representations are important in practice to succinctly summarize the pool of customers and the pool of items. In NNMs, user representations are expressive because each user's preference can be regarded as normalized mixture of preferences of stereotypical users. The interpretability of item and user representations allow us to arrange properties of items (e.g., genres of movies or topics of documents) or users (e.g., personality traits) hierarchically.

260. Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization [PDF] 返回目录
AAAI 2016. Technical Papers: Machine Learning Applications
Xin Wang, Roger Donaldson, Christopher Nell, Peter Gorniak, Martin Ester, Jiajun Bu
Social networks often provide group features to help users with similar interests associate and consume content together. Recommending groups to users poses challenges due to their complex relationship: user-group affinity is typically measured implicitly and varies with time; similarly, group characteristics change as users join and leave. To tackle these challenges, we adapt existing matrix factorization techniques to learn user-group affinity based on two different implicit engagement metrics: (i) which group-provided content users consume; and (ii) which content users provide to groups. To capture the temporally extended nature of group engagement we implement a time-varying factorization. We test the assertion that latent preferences for groups and users are sparse in investigating elastic-net regularization. Experiments using data from DeviantArt indicate that the time-varying implicit engagement-based model provides the best top-K group recommendations, illustrating the benefit of the added model complexity.

261. Multi-Domain Active Learning for Recommendation [PDF] 返回目录
AAAI 2016. Technical Papers: Machine Learning Methods
Zihan Zhang, Xiaoming Jin, Lianghao Li, Guiguang Ding, Qiang Yang
Recently, active learning has been applied to recommendation to deal with data sparsity on a single domain. In this paper, we propose an active learning strategy for recommendation to alleviate the data sparsity in a multi-domain scenario. Specifically, our proposed active learning strategy simultaneously consider both specific and independent knowledge over all domains. We use the expected entropy to measure the generalization error of the domain-specific knowledge and propose a variance-based strategy to measure the generalization error of the domain-independent knowledge. The proposed active learning strategy use a unified function to effectively combine these two measurements. We compare our strategy with five state-of-the-art baselines on five different multi-domain recommendation tasks, which are constituted by three real-world data sets. The experimental results show that our strategy performs significantly better than all the baselines and reduces human labeling efforts by at least 5.6%, 8.3%, 11.8%, 12.5% and 15.4% on the five tasks, respectively.

262. Improving Recommendation of Tail Tags for Questions in Community Question Answering [PDF] 返回目录
AAAI 2016. Technical Papers: NLP and Text Mining
Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou
We study tag recommendation for questions in community question answering (CQA). Tags represent the semantic summarization of questions are useful for navigation and expert finding in CQA and can facilitate content consumption such as searching and mining in these web sites. The task is challenging, as both questions and tags are short and a large fraction of tags are tail tags which occur very infrequently. To solve these problems, we propose matching questions and tags not only by themselves, but also by similar questions and similar tags. The idea is then formalized as a model in which we calculate question-tag similarity using a linear combination of similarity with similar questions and tags weighted by tag importance.Question similarity, tag similarity, and tag importance are learned in a supervised random walk framework by fusing multiple features. Our model thus can not only accurately identify question-tag similarity for head tags, but also improve the accuracy of recommendation of tail tags. Experimental results show that the proposed method significantly outperforms state-of-the-art methods on tag recommendation for questions. Particularly, it improves tail tag recommendation accuracy by a large margin.

263. News Citation Recommendation with Implicit and Explicit Semantics [PDF] 返回目录
ACL 2016. Long Papers
Hao Peng, Jing Liu, Chin-Yew Lin

264. User Embedding for Scholarly Microblog Recommendation [PDF] 返回目录
ACL 2016. Short Papers
Yang Yu, Xiaojun Wan, Xinjie Zhou

265. Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention [PDF] 返回目录
COLING 2016.
Haoran Huang, Qi Zhang, Yeyun Gong, Xuanjing Huang
On microblogging services, people usually use hashtags to mark microblogs, which have a specific theme or content, making them easier for users to find. Hence, how to automatically recommend hashtags for microblogs has received much attention in recent years. Previous deep neural network-based hashtag recommendation approaches converted the task into a multi-class classification problem. However, most of these methods only took the microblog itself into consideration. Motivated by the intuition that the history of users should impact the recommendation procedure, in this work, we extend end-to-end memory networks to perform this task. We incorporate the histories of users into the external memory and introduce a hierarchical attention mechanism to select more appropriate histories. To train and evaluate the proposed method, we also construct a dataset based on microblogs collected from Twitter. Experimental results demonstrate that the proposed methods can significantly outperform state-of-the-art methods. By incorporating the hierarchical attention mechanism, the relative improvement in the proposed method over the state-of-the-art method is around 67.9% in the F1-score.

266. Anecdote Recognition and Recommendation [PDF] 返回目录
COLING 2016.
Wei Song, Ruiji Fu, Lizhen Liu, Hanshi Wang, Ting Liu
We introduce a novel task Anecdote Recognition and Recommendation. An anecdote is a story with a point revealing account of an individual person. Recommending proper anecdotes can be used as evidence to support argumentative writing or as a clue for further reading. We represent an anecdote as a structured tuple — < person, story, implication >. Anecdote recognition runs on archived argumentative essays. We extract narratives containing events of a person as the anecdote story. More importantly, we uncover the anecdote implication, which reveals the meaning and topic of an anecdote. Our approach depends on discourse role identification. Discourse roles such as thesis, main ideas and support help us locate stories and their implications in essays. The experiments show that informative and interpretable anecdotes can be recognized. These anecdotes are used for anecdote recommendation. The anecdote recommender can recommend proper anecdotes in response to given topics. The anecdote implication contributes most for bridging user interested topics and relevant anecdotes.

267. Hashtag Recommendation with Topical Attention-Based LSTM [PDF] 返回目录
COLING 2016.
Yang Li, Ting Liu, Jing Jiang, Liang Zhang
Microblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing methods depend on hand-crafted features. Motivated by the successful use of long short-term memory (LSTM) for many natural language processing tasks, in this paper, we adopt LSTM to learn the representation of a microblog post. Observing that hashtags indicate the primary topics of microblog posts, we propose a novel attention-based LSTM model which incorporates topic modeling into the LSTM architecture through an attention mechanism. We evaluate our model using a large real-world dataset. Experimental results show that our model significantly outperforms various competitive baseline methods. Furthermore, the incorporation of topical attention mechanism gives more than 7.4% improvement in F1 score compared with standard LSTM method.

268. Transferring User Interests Across Websites with Unstructured Text for Cold-Start Recommendation [PDF] 返回目录
EMNLP 2016.
Yu-Yang Huang, Shou-De Lin

269. Learning to refine text based recommendations [PDF] 返回目录
EMNLP 2016.
Youyang Gu, Tao Lei, Regina Barzilay, Tommi Jaakkola

270. Session-based Recommendations with Recurrent Neural Networks [PDF] 返回目录
ICLR 2016.
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk
We apply recurrent neural networks (RNN) on a new domain, namely recommendersystems. Real-life recommender systems often face the problem of having to baserecommendations only on short session-based data (e.g. a small sportswarewebsite) instead of long user histories (as in the case of Netflix). In thissituation the frequently praised matrix factorization approaches are notaccurate. This problem is usually overcome in practice by resorting toitem-to-item recommendations, i.e. recommending similar items. We argue that bymodeling the whole session, more accurate recommendations can be provided. Wetherefore propose an RNN-based approach for session-based recommendations. Ourapproach also considers practical aspects of the task and introduces severalmodifications to classic RNNs such as a ranking loss function that make it moreviable for this specific problem. Experimental results on two data-sets showmarked improvements over widely used approaches.

271. Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design [PDF] 返回目录
ICML 2016.
William Hoiles, Mihaela van der Schaar
Successfully recommending personalized course schedules is a difficult problem given the diversity of students knowledge, learning behaviour, and goals. This paper presents personalized course recommendation and curriculum design algorithms that exploit logged student data. The algorithms are based on the regression estimator for contextual multi-armed bandits with a penalized variance term. Guarantees on the predictive performance of the algorithms are provided using empirical Bernstein bounds. We also provide guidelines for including expert domain knowledge into the recommendations. Using undergraduate engineering logged data from a post-secondary institution we illustrate the performance of these algorithms.

272. Recommendations as Treatments: Debiasing Learning and Evaluation [PDF] 返回目录
ICML 2016.
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims
Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation system itself. In this paper, we provide a principled approach to handle selection biases by adapting models and estimation techniques from causal inference. The approach leads to unbiased performance estimators despite biased data, and to a matrix factorization method that provides substantially improved prediction performance on real-world data. We theoretically and empirically characterize the robustness of the approach, and find that it is highly practical and scalable.

273. Constraint Acquisition with Recommendation Queries [PDF] 返回目录
IJCAI 2016.
Abderrazak Daoudi, Younes Mechqrane, Christian Bessiere, Nadjib Lazaar, El-Houssine Bouyakhf
Constraint acquisition systems assist the non-expert user in modeling her problem as a constraint network. Most existing constraint acquisition systems interact with the user by asking her to classify an example as positive or negative. Such queries do not use the structure of the problem and can thus lead the user to answer a large number of queries. In this paper, we propose Predict&Ask, an algorithm based on the prediction of missing constraints in the partial network learned so far. Such missing constraints are directly asked to the user through recommendation queries, a new, more informative kind of queries. Predict&Ask can be plugged in any constraint acquisition system. We experimentally compare the QuAcq system to an extended version boosted by the use of our recommendation queries. The results show that the extended version improves the basic QuAcq.

274. Exploring the Context of Locations for Personalized Location Recommendations [PDF] 返回目录
IJCAI 2016.
Xin Liu, Yong Liu, Xiaoli Li
Conventional location recommendation models rely on users' visit history, geographical influence, temporal influence, etc., to infer users' preferences for locations. However, systematically modeling a location's context (i.e., the set of locations visited before or after this location) is relatively unexplored. In this paper, by leveraging the Skip-gram model, we learn the latent representation for a location to capture the influence of its context. A pair-wise ranking loss that considers the confidences of observed user preferences for locations is then proposed to learn users' latent representations for personalized top-N location recommendations. Moreover, we also extend our model by taking into account temporal influence. Stochastic gradient descent based optimization algorithms are developed to fit the models. We conduct comprehensive experiments over four real datasets. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art location recommendation methods.

275. Cold-Start Recommendations for Audio News Stories Using Matrix Factorization [PDF] 返回目录
IJCAI 2016.
Ehsan Mohammady Ardehaly, Aron Culotta, Vivek Sundararaman, Alwar Narayanan
We investigate a suite of recommendation algorithms for audio news listening applications. This domain presents several challenges that distinguish it from more commonly studied applications such as movie recommendations: (1) we do not receive explicit rating feedback, instead only observing when a user skips a story; (2) new stories arrive continuously, increasing the importance of making recommendations for items with few observations (the cold start problem); (3) story attributes have high dimensionality, making it challenging to identify similar stories. To address the first challenge, we formulate the problem as predicting the percentage of a story a user will listen to; to address the remaining challenges, we propose several matrix factorization algorithms that cluster users, n-grams, and stories simultaneously, while optimizing prediction accuracy. We empirically evaluate our approach on a dataset of 50K users, 26K stories, and 975K interactions collected over a five month period. We find that while simple models work well for stories with many observations, our proposed approach performs best for stories with few ratings, which is critical for the real-world deployment of such an application.

276. A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback [PDF] 返回目录
IJCAI 2016.
Huayu Li, Richang Hong, Defu Lian, Zhiang Wu, Meng Wang, Yong Ge
Implicit feedback based recommendation has recently been an important task with the accumulated user-item interaction data. However, it is very challenging to produce recommendations from implicit feedback due to the sparseness of data and the lack of negative feedback/rating. Although various factor models have been proposed to tackle this problem, they either focus on rating prediction that may lead to inaccurate top-k recommendations or are dependent on the sampling of negative feedback that often results in bias. To this end, we propose a Relaxed Ranking-based Factor Model, RRFM, to relax pairwise ranking into a SVM-like task, where positive and negative feedbacks are separated by the soft boundaries, and their non-separate property is employed to capture the characteristic of unobserved data. A smooth and scalable algorithm is developed to solve group- and instance- level's optimization and parameter estimation. Extensive experiments based on real-world datasets demonstrate the effectiveness and advantage of our approach.

277. Constrained Preference Embedding for Item Recommendation [PDF] 返回目录
IJCAI 2016.
Xin Wang, Congfu Xu, Yunhui Guo, Hui Qian
To learn users' preference, their feedback information is commonly modeled as scalars and integrated into matrix factorization (MF) based algorithms. Based on MF techniques, the preference degree is computed by the product of user and item vectors, which is also represented by scalars. On the contrary, in this paper, we express users' feedback as constrained vectors, and call the idea constrained preference embedding (CPE); it means that we regard users, items and all users' behavior as vectors. We find that this viewpoint is more flexible and powerful than traditional MF for item recommendation. For example, by the proposed assumption, users' heterogeneous actions can be coherently mined because all entities and actions can be transferred to a space of the same dimension. In addition, CPE is able to model the feedback of uncertain preference degree. To test our assumption, we propose two models called CPE-s and CPE-ps based on CPE for item recommendation, and show that the popular pair-wise ranking model BPR-MF can be deduced by some restrictions and variations on CPE-s. In the experiments, we will test CPE and the proposed algorithms, and prove their effectiveness.

278. Improving Top-N Recommendation with Heterogeneous Loss [PDF] 返回目录
IJCAI 2016.
Feipeng Zhao, Yuhong Guo
Personalized top-N recommendation systems have great impact on many real world applications such as E-commerce platforms and social networks. Most existing methods produce personalized top-N recommendations by minimizing a specific uniform loss such as pairwise ranking loss or pointwise recovery loss. In this paper, we propose a novel personalized top-N recommendation approach that minimizes a combined heterogeneous loss based on linear self-recovery models. The heterogeneous loss integrates the strengths of both pairwise ranking loss and pointwise recovery loss to provide more informative recommendation predictions. We formulate the learning problem with heterogeneous loss as a constrained convex minimization problem and develop a projected stochastic gradient descent optimization algorithm to solve it. We evaluate the proposed approach on a set of personalized top-N recommendation tasks. The experimental results show the proposed approach outperforms a number of state-of-the-art methods on top-N recommendation.

279. Hashtag Recommendation Using Attention-Based Convolutional Neural Network [PDF] 返回目录
IJCAI 2016.
Yuyun Gong, Qi Zhang
Along with the increasing requirements, the hashtag recommendation task for microblogs has been receiving considerable attention in recent years. Various researchers have studied the problem from different aspects. However, most of these methods usually need handcrafted features. Motivated by the successful use of convolutional neural networks (CNNs) for many natural language processing tasks, in this paper, we adopt CNNs to perform the hashtag recommendation problem. To incorporate the trigger words whose effectiveness have been experimentally evaluated in several previous works, we propose a novel architecture with an attention mechanism. The results of experiments on the data collected from a real world microblogging service demonstrated that the proposed model outperforms state-of-the-art methods. By incorporating trigger words into the consideration, the relative improvement of the proposed method over the state-of-the-art method is around 9.4% in the F1-score.

280. Generating Recommendation Evidence Using Translation Model [PDF] 返回目录
IJCAI 2016.
Jizhou Huang, Shiqi Zhao, Shiqiang Ding, Haiyang Wu, Mingming Sun, Haifeng Wang
Entity recommendation, providing entity suggestions relevant to the query that a user is searching for, has become a key feature of today's web search engine. Despite the fact that related entities are relevant to users' search queries, sometimes users cannot easily understand the recommended entities without evidences. This paper proposes a statistical model consisting of four sub-models to generate evidences for entities, which can help users better understand each recommended entity, and figure out the connections between the recommended entities and a given query. The experiments show that our method is domain independent, and can generate catchy and interesting evidences in the application of entity recommendation.

281. Latent Contextual Bandits and their Application to Personalized Recommendations for New Users [PDF] 返回目录
IJCAI 2016.
Li Zhou, Emma Brunskill
Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally rely on features alone to capture user variability. Such methods are inefficient in learning new users' interests. In this paper we propose Latent Contextual Bandits. We consider both the benefit of leveraging a set of learned latent user classes for new users, and how we can learn such latent classes from prior users. We show that our approach achieves a better regret bound than existing algorithms. We also demonstrate the benefit of our approach using a large real world dataset and a preliminary user study.

282. Item Recommendation for Emerging Online Businesses [PDF] 返回目录
IJCAI 2016.
Chun-Ta Lu, Sihong Xie, Weixiang Shao, Lifang He, Philip S. Yu
Nowadays, a large number of new online businesses emerge rapidly. For these emerging businesses, existing recommendation models usually suffer from the data-sparsity. In this paper, we introduce a novel similarity measure, AmpSim (Augmented Meta Path-based Similarity) that takes both the linkage structures and the augmented link attributes into account. By traversing between heterogeneous networks through overlapping entities, AmpSim can easily gather side information from other networks and capture the rich similarity semantics between entities. We further incorporate the similarity information captured by AmpSim in a collective matrix factorization model such that the transferred knowledge can be iteratively propagated across networks to fit the emerging business. Extensive experiments conducted on real-world datasets demonstrate that our method significantly outperforms other state-of-the-art recommendation models in addressing item recommendation for emerging businesses.

283. Collaborative Evolution for User Profiling in Recommender Systems [PDF] 返回目录
IJCAI 2016.
Zhongqi Lu, Sinno Jialin Pan, Yong Li, Jie Jiang, Qiang Yang
Accurate user profiling is important for an online recommender system to provide proper personalized recommendations to its users. In many real-world scenarios, the user's interests towards the items may change over time. Therefore, a dynamic and evolutionary user profile is needed. In this work, we come up with a novel evolutionary view of user's profile by proposing a Collaborative Evolution (CE) model, which learns the evolution of user's profiles through the sparse historical data in recommender systems and outputs the prospective user profile of the future. To verify the effectiveness of the proposed model, we conduct experiments on a real-world dataset, which is obtained from the online shopping website of Tencent — www.51buy.com and contains more than 1 million users' shopping records in a time span of more than 180 days. Experimental analyses demonstrate that our proposed CE model can be used to make better future recommendations compared to several state-of-the-art methods.

284. A Framework for Recommending Relevant and Diverse Items [PDF] 返回目录
IJCAI 2016.
Chaofeng Sha, Xiaowei Wu, Junyu Niu
The traditional recommendation systems usually aim to improve the recommendation accuracy while overlooking the diversity within the recommended lists. Although some diversification techniques have been designed to recommend top-k items in terms of both relevance and diversity, the coverage of the user's interest is overlooked. In this paper, we propose a general framework to recommend relevant and diverse items which explicitly takes the coverage of user interest into account. Based on the theoretical analysis, we design efficient greedy algorithms to get the near optimal solutions for those NP-hard problems. Experimental results on MovieLens dataset demonstrate that our approach outperforms state-of-the-art techniques in terms of both precision and diversity.

285. Matrix Factorization+ for Movie Recommendation [PDF] 返回目录
IJCAI 2016.
Lili Zhao, Zhongqi Lu, Sinno Jialin Pan, Qiang Yang
We present a novel model for movie recommendations using additional visual features extracted from pictorial data like posters and still frames, to better understand movies. In particular, several context-based methods for recommendation are shown to be special cases of our proposed framework. Unlike existing context-based approaches, our method can be used to incorporate visual features — features that are lacking in existing context-based approaches for movie recommendations. In reality, movie posters and still frames provide us with rich knowledge for understanding movies, users' preferences as well. For instance, user may want to watch a movie at the minute when she/he finds some released posters or still frames attractive. Unfortunately, such unique features cannot be revealed from rating data or other form of context that being used in most of existing methods. In this paper, we take a step in this direction and investigate both low-level and high-level visual features from the movie posters and still frames for further improvement of recommendation methods. A comprehensive set of experiments on real world datasets shows that our approach leads to significant improvement over the state-of-the-art methods.

286. Adaptive Sequential Recommendation Using Context Trees [PDF] 返回目录
IJCAI 2016.
Fei Mi, Boi Faltings
Machine learning is often used to acquire knowledge in domains that undergo frequent changes, such as networks, social media, or markets. These frequent changes poses a challenge to most machine learning methods as they have difficulty adapting. So my thesis topic focus on adaptive machine learning models. At the first step, we consider a forum content recommender system for massive open online courses (MOOCs) as an example of an application where recommendations have to adapt to new items and evolving user preferences. We formalize the recommendation problem as a sequence prediction problem and compare different recommendation methods, including a new method called context tree (CT). The results show that the CT recommender performs much better than other methods. We analyze the reasons for this and demonstrate that it is because of better adaptation to changes in the domain.

287. PARecommender: A Pattern-Based System for Route Recommendation [PDF] 返回目录
IJCAI 2016.
Feiyi Tang, Jia Zhu, Yang Cao, Sanli Ma, Yulong Chen, Jing He, Changqin Huang, Gansen Zhao, Yong Tang
Widely adoption of GPS-enabled devices generates massive trajectory data every minute. The trajectory data can generate meaningful traffic patterns. In this demo, we present a system called PARecommender, which predicts traffic conditions and provides route recommendation based on generated traffic patterns. We first introduce the technical details of PARecommender, and then show several real cases that how PARecommender works.

288. Cross-media Event Extraction and Recommendation [PDF] 返回目录
NAACL 2016. Demonstrations
Di Lu, Clare Voss, Fangbo Tao, Xiang Ren, Rachel Guan, Rostyslav Korolov, Tongtao Zhang, Dongang Wang, Hongzhi Li, Taylor Cassidy, Heng Ji, Shih-fu Chang, Jiawei Han, William Wallace, James Hendler, Mei Si, Lance Kaplan

289. Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks [PDF] 返回目录
NeurIPS 2016.
Hao Wang, Xingjian SHI, Dit-Yan Yeung
Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.

290. Deconvolving Feedback Loops in Recommender Systems [PDF] 返回目录
NeurIPS 2016.
Ayan Sinha, David F. Gleich, Karthik Ramani
Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences. Recommender systems then use this information to make personalized suggestions to users. When users accept these recommendations it creates a feedback loop in the recommender system, and these loops iteratively influence the collaborative filtering algorithm's predictions over time. We investigate whether it is possible to identify items affected by these feedback loops. We state sufficient assumptions to deconvolve the feedback loops while keeping the inverse solution tractable. We furthermore develop a metric to unravel the recommender system's influence on the entire user-item rating matrix. We use this metric on synthetic and real-world datasets to (1) identify the extent to which the recommender system affects the final rating matrix, (2) rank frequently recommended items, and (3) distinguish whether a user's rated item was recommended or an intrinsic preference. Our results indicate that it is possible to recover the ratings matrix of intrinsic user preferences using a single snapshot of the ratings matrix without any temporal information.

291. A Personalized Interest-Forgetting Markov Model for Recommendations [PDF] 返回目录
AAAI 2015. AI and the Web
Jun Chen, Chaokun Wang, Jianmin Wang
Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.

292. On Information Coverage for Location Category Based Point-of-Interest Recommendation [PDF] 返回目录
AAAI 2015. AI and the Web
Xuefeng Chen, Yifeng Zeng, Gao Cong, Shengchao Qin, Yanping Xiang, Yuanshun Dai
Point-of-interest(POI) recommendation becomes a valuable service in location-based social networks. Based on the norm that similar users are likely to have similar preference of POIs, the current recommendation techniques mainly focus on users' preference to provide accurate recommendation results. This tends to generate a list of homogeneous POIs that are clustered into a narrow band of location categories(like food, museum, etc.) in a city. However, users are more interested to taste a wide range of flavors that are exposed in a global set of location categories in the city.In this paper, we formulate a new POI recommendation problem, namely top-K location category based POI recommendation, by introducing information coverage to encode the location categories of POIs in a city.The problem is NP-hard. We develop a greedy algorithm and further optimization to solve this challenging problem. The experimental results on two real-world datasets demonstrate the utility of new POI recommendations and the superior performance of the proposed algorithms.

293. COT: Contextual Operating Tensor for Context-Aware Recommender Systems [PDF] 返回目录
AAAI 2015. AI and the Web
Qiang Liu, Shu Wu, Liang Wang
With rapid growth of information on the internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information can be used as a significant factor in modeling user behavior, various context-aware recommendation methods are proposed. However, the state-of-the-art context modeling methods treat contexts as other dimensions similar to the dimensions of users and items, and cannot capture the special semantic operation of contexts. On the other hand, some works on multi-domain relation prediction can be used for the context-aware recommendation, but they have problems in generating recommendation under a large amount of contextual information. In this work, we propose Contextual Operating Tensor (COT) model, which represents the common semantic effects of contexts as a contextual operating tensor and represents a context as a latent vector. Then, to model the semantic operation of a context combination, we generate contextual operating matrix from the contextual operating tensor and latent vectors of contexts. Thus latent vectors of users and items can be operated by the contextual operating matrices. Experimental results show that the proposed COT model yields significant improvements over the competitive compared methods on three typical datasets, i.e., Food, Adom and Movielens-1M datasets.

294. Content-Based Collaborative Filtering for News Topic Recommendation [PDF] 返回目录
AAAI 2015. AI and the Web
Zhongqi Lu, Zhicheng Dou, Jianxun Lian, Xing Xie, Qiang Yang
News recommendation has become a big attraction with which major Web search portals retain their users. Two effective approaches are Content-based Filtering and Collaborative Filtering, each serving a specific recommendation scenario. The Content-based Filtering approaches inspect rich contexts of the recommended items, while the Collaborative Filtering approaches predict the interests of long-tail users by collaboratively learning from interests of related users. We have observed empirically that, for the problem of news topic displaying, both the rich context of news topics and the long-tail users exist. Therefore, in this paper, we propose a Content-based Collaborative Filtering approach (CCF) to bring both Content-based Filtering and Collaborative Filtering approaches together. We found that combining the two is not an easy task, but the benefits of CCF are impressive. On one hand, CCF makes recommendations based on the rich contexts of the news. On the other hand, CCF collaboratively analyzes the scarce feedbacks from the long-tail users. We tailored this CCF approach for the news topic displaying on the Bing front page and demonstrated great gains in attracting users. In the experiments and analyses part of this paper, we discuss the performance gains and insights in news topic recommendation in Bing.

295. Recommending Positive Links in Signed Social Networks by Optimizing a Generalized AUC [PDF] 返回目录
AAAI 2015. AI and the Web
Dongjin Song, David A. Meyer
With the rapid development of signed social networks in which therelationships between two nodes can be either positive (indicatingrelations such as like) or negative (indicating relations such asdislike), producing a personalized ranking list with positive linkson the top and negative links at the bottom is becoming anincreasingly important task. To accomplish it, we propose ageneralized AUC (GAUC) to quantify the ranking performance ofpotential links (including positive, negative, and unknown statuslinks) in partially observed signed social networks. In addition, wedevelop a novel link recommendation algorithm by directly optimizingthe GAUC loss. We conduct experimental studies based upon Wikipedia,MovieLens, and Slashdot; our results demonstrate the effectivenessand the efficiency of the proposed approach.

296. Are Features Equally Representative? A Feature-Centric Recommendation [PDF] 返回目录
AAAI 2015. AI and the Web
Chenyi Zhang, Ke Wang, Ee-Peng Lim, Qinneng Xu, Jianling Sun, Hongkun Yu
Typically a user prefers an item (e.g., a movie) because she likes certain features of the item (e.g., director, genre, producer). This observation motivates us to consider a feature-centric recommendation approach to item recommendation: instead of directly predicting the rating on items, we predict the rating on the features of items, and use such ratings to derive the rating on an item. This approach offers several advantages over the traditional item-centric approach: it incorporates more information about why a user chooses an item, it generalizes better due to the denser feature rating data, it explains the prediction of item ratings through the predicted feature ratings. Another contribution is turning a principled item-centric solution into a feature-centric solution, instead of inventing a new algorithm that is feature-centric. This approach maximally leverages previous research. We demonstrate this approach by turning the traditional item-centric latent factor model into a feature-centric solution and demonstrate its superiority over item-centric approaches.

297. Personalized Tag Recommendation through Nonlinear Tensor Factorization Using Gaussian Kernel [PDF] 返回目录
AAAI 2015. Applications
Xiaomin Fang, Rong Pan, Guoxiang Cao, Xiuqiang He, Wenyuan Dai
Personalized tag recommendation systems recommend a list of tags to a user when he is about to annotate an item. It exploits the individual preference and the characteristic of the items. Tensor factorization tech- niques have been applied to many applications, such as tag recommendation. Models based on Tucker Decomposition can achieve good performance but require a lot of computation power. On the other hand, mod- els based on Canonical Decomposition can run in linear time and are more feasible for online recommendation. In this paper, we propose a novel method for personalized tag recommendation, which can be considered as a nonlinear extension of Canonical Decomposition. Different from linear tensor factorization, we exploit Gaussian radial basis function to increase the model’s capacity. The experimental results show that our proposed method outperforms the state-of-the-art methods for tag recommendation on real datasets and perform well even with a small number of features, which verifies that our models can make better use of features.

298. Content-Aware Point of Interest Recommendation on Location-Based Social Networks [PDF] 返回目录
AAAI 2015. Machine Learning Applications
Huiji Gao, Jiliang Tang, Xia Hu, Huan Liu
The rapid urban expansion has greatly extended the physical boundary of users' living area and developed a large number of POIs (points of interest). POI recommendation is a task that facilitates users' urban exploration and helps them filter uninteresting POIs for decision making. While existing work of POI recommendation on location-based social networks (LBSNs) discovers the spatial, temporal, and social patterns of user check-in behavior, the use of content information has not been systematically studied. The various types of content information available on LBSNs could be related to different aspects of a user's check-in action, providing a unique opportunity for POI recommendation. In this work, we study the content information on LBSNs w.r.t. POI properties, user interests, and sentiment indications. We model the three types of information under a unified POI recommendation framework with the consideration of their relationship to check-in actions. The experimental results exhibit the significance of content information in explaining user behavior, and demonstrate its power to improve POI recommendation performance on LBSNs.

299. Finding a Collective Set of Items: From Proportional Multirepresentation to Group Recommendation [PDF] 返回目录
AAAI 2015. Multiagent Systems
Piotr Krzysztof Skowron, Piotr Faliszewski, Jérôme Lang
We consider the following problem: There is a set of items (e.g., movies) and a group of agents (e.g., passengers on a plane); each agent has some intrinsic utility for each of the items. Our goal is to pick a set of K items that maximize the total derived utility of all the agents (i.e., in our example we are to pick K movies that we put on the plane's entertainment system). However, the actual utility that an agent derives from a given item is only a fraction of its intrinsic one, and this fraction depends on how the agent ranks the item among the chosen, available, ones. We provide a formal specification of the model and provide concrete examples and settings where it is applicable. We show that the problem is hard in general, but we show a number of tractability results for its natural special cases.

300. A Neural Probabilistic Model for Context Based Citation Recommendation [PDF] 返回目录
AAAI 2015. NLP and Text Mining
Wenyi Huang, Zhaohui Wu, Chen Liang, Prasenjit Mitra, C. Lee Giles
Automatic citation recommendation can be very useful for authoring a paper and is an AI-complete problem due to the challenge of bridging the semantic gap between citation context and the cited paper. It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context. To help with this problem, we propose a novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers. The probability of citing a paper given a citation context is estimated by training a multi-layer neural network. We implement and evaluate our model on the entire CiteSeer dataset, which at the time of this work consists of 10,760,318 citation contexts from 1,017,457 papers. We show that the proposed model significantly outperforms other state-of-the-art models in recall, MAP, MRR, and nDCG.

301. Learning to Recommend Quotes for Writing [PDF] 返回目录
AAAI 2015. NLP and Text Mining
Jiwei Tan, Xiaojun Wan, Jianguo Xiao
In this paper, we propose and address a novel task of recommending quotes for writing. Quote is short for quotation, which is the repetition of someone else’s statement or thoughts. It is a common case in our writing when we would like to cite someone’s statement, like a proverb or a statement by some famous people, to make our composition more elegant or convincing. However, sometimes we are so eager to make a citation of quote somewhere, but have no idea about the relevant quote to express our idea. Because knowing or remembering so many quotes is not easy, it is exciting to have a system to recommend relevant quotes for us while writing. In this paper we tackle this appealing AI task, and build up a learning framework for quote recommendation. We collect abundant quotes from the Internet, and mine real contexts containing these quotes from large amount of electronic books, to build up a dataset for experiments. We explore the particular features of this task, and propose a few useful features to model the characteristics of quotes and the relevance of quotes to contexts. We apply a supervised learning to rank model to integrate multiple features. Experiment results show that, our proposed approach is appropriate for this task and it outperforms other recommendation methods.

302. Relational Stacked Denoising Autoencoder for Tag Recommendation [PDF] 返回目录
AAAI 2015. Novel Machine Learning Algorithms
Hao Wang, Xingjian Shi, Dit-Yan Yeung
Tag recommendation has become one of the most important ways of organizing and indexing online resources like articles, movies, and music. Since tagging information is usually very sparse, effective learning of the content representation for these resources is crucial to accurate tag recommendation. Recently, models proposed for tag recommendation, such as collaborative topic regression and its variants, have demonstrated promising accuracy. However, a limitation of these models is that, by using topic models like latent Dirichlet allocation as the key component, the learned representation may not be compact and effective enough. Moreover, since relational data exist as an auxiliary data source in many applications, it is desirable to incorporate such data into tag recommendation models. In this paper, we start with a deep learning model called stacked denoising autoencoder (SDAE) in an attempt to learn more effective content representation. We propose a probabilistic formulation for SDAE and then extend it to a relational SDAE (RSDAE) model. RSDAE jointly performs deep representation learning and relational learning in a principled way under a probabilistic framework. Experiments conducted on three real datasets show that both learning more effective representation and learning from relational data are beneficial steps to take to advance the state of the art.

303. Coupled Collaborative Filtering for Context-aware Recommendation [PDF] 返回目录
AAAI 2015. Student Abstracts
Xinxin Jiang, Wei Liu, Longbing Cao, Guodong Long
Context-aware features have been widely recognized as important factors in recommender systems. However, as a major technique in recommender systems, traditional Collaborative Filtering (CF) does not provide a straight-forward way of integrating the context-aware information into personal recommendation. We propose a Coupled Collaborative Filtering (CCF) model to measure the contextual information and use it to improve recommendations. In the proposed approach, coupled similarity computation is designed to be calculated by interitem, intra-context and inter-context interactions among item, user and context-ware factors. Experiments based on different types of CF models demonstrate the effectiveness of our design.

304. Improving Cross-Domain Recommendation through Probabilistic Cluster-Level Latent Factor Model [PDF] 返回目录
AAAI 2015. Student Abstracts
Siting Ren, Sheng Gao, Jianxin Liao, Jun Guo
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the user-item co-clustering. To capture diversities among different domains, we propose a novel Probabilistic Cluster-level Latent Factor (PCLF) model to improve the cross-domain recommendation performance. Experiments on several real world datasets demonstrate that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task.

305. VecLP: A Realtime Video Recommendation System for Live TV Programs [PDF] 返回目录
AAAI 2015. Demonstrations
Sheng Gao, Dai Zhang, Honggang Zhang, Chao Huang, Yongsheng Zhang, Jianxin Liao, Jun Guo
We propose VecLP, a novel Internet Video recommendation system working for Live TV Programs in this paper. Given little information on the live TV programs, our proposed VecLP system can effectively collect necessary information on both the programs and the subscribers as well as a large volume of related online videos, and then recommend the relevant Internet videos to the subscribers. For that, the key frames are firstly detected from the live TV programs, and then visual and textual features are extracted from these frames to enhance the understanding of the TV broadcasts. Furthermore, by utilizing the subscribers' profiles and their social relationships, a user preference model is constructed, which greatly improves the diversity of the recommendations in our system. The subscriber's browsing history is also recorded and used to make a further personalized recommendation. This work also illustrates how our proposed VecLP system makes it happen. Finally, we dispose some sort of new recommendation strategies in use at the system to meet special needs from diverse live TV programs and throw light upon how to fuse these strategies.

306. Hashtag Recommendation Using Dirichlet Process Mixture Models Incorporating Types of Hashtags [PDF] 返回目录
EMNLP 2015.
Yeyun Gong, Qi Zhang, Xuanjing Huang

307. LDTM: A Latent Document Type Model for Cumulative Citation Recommendation [PDF] 返回目录
EMNLP 2015.
Jingang Wang, Dandan Song, Zhiwei Zhang, Lejian Liao, Luo Si, Chin-Yew Lin

308. Towards City-Scale Mobile Crowdsourcing: Task Recommendations under Trajectory Uncertainties [PDF] 返回目录
IJCAI 2015.
Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra
https://www.ijcai.org/In this work, we investigate the problem of large-scale mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually pick tasks to perform, we automatically make task recommendation based on workers' historical trajectories and desired time budgets. The challenge of predicting workers' trajectories is that it is faced with uncertainties, as a worker does not take same routes every day. In this work, we depart from deterministic modeling and study the stochastic task recommendation problem where each worker is associated with several predicted routine routes with probabilities. We formulate this problem as a stochastic integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures of the formulation and apply the Lagrangian relaxation technique to scale up computation. Experiments have been performed over the instances generated using the real Singapore transportation network. The results show that we can find significantly better solutions than the deterministic formulation.

309. Optimal Greedy Diversity for Recommendation [PDF] 返回目录
IJCAI 2015.
Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen
https://www.ijcai.org/The need for diversification manifests in various recommendation use cases. In this work, we propose a novel approach to diversifying a list of recommended items, which maximizes the utility of the items subject to the increase in their diversity. From a technical perspective, the problem can be viewed as maximization of a modular function on the polytope of a submodular function, which can be solved optimally by a greedy method. We evaluate our approach in an offline analysis, which incorporates a number of baselines and metrics, and in two online user studies. In all the experiments, our method outperforms the baseline methods.

310. Music Recommenders: User Evaluation Without Real Users? [PDF] 返回目录
IJCAI 2015.
Susan Craw, Ben Horsburgh, Stewart Massie
https://www.ijcai.org/Good music recommenders should not only suggest quality recommendations, but should also allow users to discover new/niche music. User studies capture explicit feedback on recommendation quality and novelty, but can be expensive, and may have difficulty replicating realistic scenarios. Lack of effective offline evaluation methods restricts progress in music recommendation research. The challenge is finding suitable measures to score recommendation quality, and in particular avoiding popularity bias, whereby the quality is not recognised when the track is not well known. This paper presents a low cost method that leverages available social media data and shows it to be effective. Not only is it based on explicit feedback from many users, but it also overcomes the popularity bias that disadvantages new/niche music. Experiments show that its findings are consistent with those from an online study with real users. In comparisons with other offline measures, the social media score is shown to be a more reliable proxy for opinions of real users. Its impact on music recommendation is its ability to recognise recommenders that enable discovery, as well as suggest quality recommendations.

311. A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews [PDF] 返回目录
IJCAI 2015.
Guang-Neng Hu, Xin-Yu Dai, Yunya Song, Shujian Huang, Jiajun Chen
https://www.ijcai.org/Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social MF) can integrate ratings with social relations and topic matrix factorization can integrate ratings with item reviews, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the two approaches, in two steps. First, we extend Social MF to exploit the graph structure of neighbors. Second, we propose a novel framework MR3 to jointly model these three types of information effectively for rating prediction by aligning latent factors and hidden topics. We achieve more accurate rating prediction on two real-life datasets. Furthermore, we measure the contribution of each data source to the proposed framework.

312. Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Durations [PDF] 返回目录
IJCAI 2015.
Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, Shanika Karunasekera
https://www.ijcai.org/Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select Points of Interest (POI) to visit in unfamiliar cities, and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photos. Our tour recommendation problem is modelled using a formulation of the Orienteering problem, and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations, and demonstrate how POI visit duration can be personalized using this time-based user interest. Using a Flickr dataset of four cities, our experiments show the effectiveness of PersTour against various baselines, in terms of tour popularity, interest, recall, precision and F1-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.

313. Modeling Users' Dynamic Preference for Personalized Recommendation [PDF] 返回目录
IJCAI 2015.
Xin Liu
https://www.ijcai.org/Modeling the evolution of users' preference over time is essential for personalized recommendation. Traditional time-aware models like (1) time-window or recency based approaches ignore or deemphasize much potentially useful information, and (2) time-aware collaborative filtering (CF) approaches largely rely on the information of other users, thus failing to precisely and comprehensively profile individual users for personalization. In this paper, for implicit feedback data, we propose a personalized recommendation model to capture users' dynamic preference using Gaussian process. We first apply topic modeling to represent a user's temporal preference in an interaction as a topic distribution. By aggregating such topic distributions of the user's past interactions, we build her profile, where we treat each topic's values at different interactions as a time series. Gaussian process is then applied to predict the user's preference in the next interactions for top-N recommendation. Experiments conducted over two real datasets demonstrate that our approach outperforms the state-of-the-art recommendation models by at least 42.46% and 66.14% in terms of precision and Mean Reciprocal Rank respectively.

314. A Boosting Algorithm for Item Recommendation with Implicit Feedback [PDF] 返回目录
IJCAI 2015.
Yong Liu, Peilin Zhao, Aixin Sun, Chunyan Miao
https://www.ijcai.org/Many recommendation tasks are formulated as top-N item recommendation problems based on users' implicit feedback instead of explicit feedback. Here explicit feedback refers to users' ratings to items while implicit feedback is derived from users' interactions with items, e.g., number of times a user plays a song. In this paper, we propose a boosting algorithm named AdaBPR (Adaptive Boosting Personalized Ranking) for top-N item recommendation using users' implicit feedback. In the proposed framework, multiple homogeneous component recommenders are linearly combined to create an ensemble model, for better recommendation accuracy. The component recommenders are constructed based on a fixed collaborative filtering algorithm by using a re-weighting strategy, which assigns a dynamic weight distribution on the observed user-item interactions. AdaBPR demonstrates its effectiveness on three datasets compared with strong baseline algorithms.

315. Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees [PDF] 返回目录
IJCAI 2015.
Georgios Theocharous, Philip S. Thomas, Mohammad Ghavamzadeh
https://www.ijcai.org/In this paper, we propose a framework for using reinforcement learning (RL) algorithms to learn good policies for personalized ad recommendation (PAR) systems. The RL algorithms take into account the long-term effect of an action, and thus, could be more suitable than myopic techniques like supervised learning and contextual bandit, for modern PAR systems in which the number of returning visitors is rapidly growing. However, while myopic techniques have been well-studied in PAR systems, the RL approach is still in its infancy, mainly due to two fundamental challenges: how to compute a good RL strategy and how to evaluate a solution using historical data to ensure its “safety” before deployment. In this paper, we propose to use a family of off-policy evaluation techniques with statistical guarantees to tackle both these challenges. We apply these methods to a real PAR problem, both for evaluating the final performance and for optimizing the parameters of the RL algorithm. Our results show that a RL algorithm equipped with these off-policy evaluation techniques outperforms the myopic approaches. Our results also give fundamental insights on the difference between the click through rate (CTR) and life-time value (LTV) metrics for evaluating the performance of a PAR algorithm.

316. Exploring Implicit Hierarchical Structures for Recommender Systems [PDF] 返回目录
IJCAI 2015.
Suhang Wang, Jiliang Tang, Yilin Wang, Huan Liu
https://www.ijcai.org/Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user preferences. Thus, there's a gap between the importance of hierarchical structures and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework HSR to bridge the gap, which enables us to capture the implicit hierarchical structures of users and items simultaneously. Experimental results on two real world datasets demonstrate the effectiveness of the proposed framework.

317. Recommendation Algorithms for Optimizing Hit Rate, User Satisfaction and Website Revenue [PDF] 返回目录
IJCAI 2015.
Xin Wang, Yunhui Guo, Congfu Xu
https://www.ijcai.org/We generally use hit rate to measure the performance of item recommendation algorithms. In addition to hit rate, we consider another two important factors which are ignored by most previous works. First, whether users are satisfied with the recommended items. It is possible that a user has bought an item but dislikes it. Hence high hit rate does not reflect high customer satisfaction. Second, whether the website retailers are satisfied with the recommendation results. If a customer is interested in two products and wants to buy one of them, it may be better to suggest the item which can help bring more profit. Therefore, a good recommendation algorithm should not only consider improving hit rate but also consider optimizing user satisfaction and website revenue. In this paper, we propose two algorithms for the above purposes and design two modified hit rate based metrics to measure them. Experimental results on 10 real-world datasets show that our methods can not only achieve better hit rate, but improve user satisfaction and website revenue comparing with the state-of-the-art models.

318. Personalized Ranking Metric Embedding for Next New POI Recommendation [PDF] 返回目录
IJCAI 2015.
Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, Quan Yuan
https://www.ijcai.org/The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods.

319. Tackling Data Sparseness in Recommendation using Social Media based Topic Hierarchy Modeling [PDF] 返回目录
IJCAI 2015.
Xingwei Zhu, Zhaoyan Ming, Yu Hao, Xiaoyan Zhu
https://www.ijcai.org/Recommendation systems play an important role in E-Commerce. However, their potential usefulness in real world applications is greatly limited by the availability of historical rating records from the customers. This paper presents a novel method to tackle the problem of data sparseness in user ratings with rich and timely domain information from social media. We first extract multiple side information for products from their relevant social media contents. Next, we convert the information into weighted topic-item ratings and inject them into an extended latent factor based recommendation model in an optimized approach. Our evaluation on two real world datasets demonstrates the superiority of our method over state-of-the-art methods.

320. A Space Alignment Method for Cold-Start TV Show Recommendations [PDF] 返回目录
IJCAI 2015.
Shiyu Chang, Jiayu Zhou, Pirooz Chubak, Junling Hu, Thomas S. Huang
https://www.ijcai.org/In recent years, recommendation algorithms have become one of the most active research areas driven by the enormous industrial demands. Most of the existing recommender systems focus on topics such as movie, music, e-commerce etc., which essentially differ from the TV show recommendations due to the cold-start and temporal dynamics. Both effectiveness (effectively handling the cold-start TV shows) and efficiency (efficiently updating the model to reflect the temporal data changes) concerns have to be addressed to design real-world TV show recommendation algorithms. In this paper, we introduce a novel hybrid recommendation algorithm incorporating both collaborative user-item relationship as well as item content features. The cold-start TV shows can be correctly recommended to desired users via a so called space alignment technique. On the other hand, an online updating scheme is developed to utilize new user watching behaviors. We present experimental results on a real TV watch behavior data set to demonstrate the significant performance improvement over other state-of-the-art algorithms.

321. Ice-Breaking: Mitigating Cold-Start Recommendation Problem by Rating Comparison [PDF] 返回目录
IJCAI 2015.
Jingwei Xu, Yuan Yao, Hanghang Tong, XianPing Tao, Jian Lu
https://www.ijcai.org/Recommender system has become an indispensable component in many e-commerce sites. One major challenge that largely remains open is the cold-start problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RaPare) to break this ice barrier. The center-piece of our RaPare is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RaPare strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.

322. Mobile Query Recommendation via Tensor Function Learning [PDF] 返回目录
IJCAI 2015.
Zhou Zhao, Ruihua Song, Xing Xie, Xiaofei He, Yueting Zhuang
https://www.ijcai.org/With the prevalence of mobile search nowadays, the benefits of mobile query recommendation are well recognized, which provide formulated queries sticking to users’ search intent. In this paper, we introduce the problem of query recommendation on mobile devices and model the user-location-query relations with a tensor representation. Unlike previous studies based on tensor decomposition, we study this problem via tensor function learning. That is, we learn the tensor function from the side information of users, locations and queries, and then predict users’ search intent. We develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the introduced problem. We empirically evaluate our approach based on the mobile query dataset from Bing search engine in the city of Beijing, China, and show that our method can outperform several state-of-the-art approaches.

323. Measuring and Recommending Time-Sensitive Routes from Location-based Data [PDF] 返回目录
IJCAI 2015.
Hsun-Ping Hsieh, Cheng-Te Li, Shou-De Lin
https://www.ijcai.org/Location-based services allow users to perform geo-spatial recording actions, which facilitates the mining of the moving activities of human beings. This paper proposes a system, TimeRouter, to recommend time-sensitive trip routes consisting of a sequence of locations with associated time stamps based on knowledge extracted from large-scale location check-in data. We first propose a statistical route goodness measure considering: (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. Then we construct the time-sensitive route recommender with two major functions: (1) constructing the route based on the user-specified source location with the starting time, (2) composing the route between the specified source location and the destination location given a starting time. We devise a search method, Guidance Search, to derive the routes efficiently and effectively. Experiments on Gowalla check-in datasets with user study show the promising performance of our proposed route recommendation method.

324. Adapting to User Preference Changes in Interactive Recommendation [PDF] 返回目录
IJCAI 2015.
Negar Hariri, Bamshad Mobasher, Robin Burke
https://www.ijcai.org/Recommender systems have become essential tools in many application areas as they help alleviate information overload by tailoring their recommendations to users' personal preferences. Users' interests in items, however, may change over time depending on their current situation. Without considering the current circumstances of a user, recommendations may match the general preferences of the user, but they may have small utility for the user in his/her current situation. We focus on designing systems that interact with the user over a number of iterations and at each step receive feedback from the user in the form of a reward or utility value for the recommended items. The goal of the system is to maximize the sum of obtained utilities over each interaction session. We use a multi-armed bandit strategy to model this online learning problem and we propose techniques for detecting changes in user preferences. The recommendations are then generated based on the most recent preferences of a user. Our evaluation results indicate that our method can improve the existing bandit algorithms by considering the sudden variations in the user's feedback behavior.

325. A Distributed Platform to Ease the Development of Recommendation Algorithms on Large-Scale Graphs [PDF] 返回目录
IJCAI 2015.
Alejandro Corbellini
https://www.ijcai.org/The creation of novel recommendation algorithms for social networks is currently struggling with the volume of available data originating in such environments. Given that social networks can be modeled as graphs, a distributed graph-oriented support to exploit the computing capabilities of clusters arises as a necessity. In this thesis, a platform for graph storage and processing named Graphly is proposed along with GraphRec, an API for easy specification of recommendation algorithms. Graphly and GraphRec hide distributed programming concerns from the user while still allowing fine-tuning of the remote execution. For example, users may customize an algorithm execution using job distribution strategies, without modifying the original code. GraphRec also simplifies the design of graph-based recommender systems by implementing well-known algorithms as “primitives” that can be reused.

326. Rational Architecture = Architecture from a Recommender Perspective [PDF] 返回目录
IJCAI 2015.
Marc van Zee
https://www.ijcai.org/An Enterprise Architecture (EA) provides a holistic view of an enterprise. In creating or changing an EA, multiple decisions have to be made, which are based on assumptions about the situation at hand. In this thesis, we develop a framework for reasoning about changing decisions and assumptions, based on logical theories of intentions. This framework serves as the underlying formalism for a recommender system for EA decision making.

327. Efficient Thompson Sampling for Online Matrix-Factorization Recommendation [PDF] 返回目录
NeurIPS 2015.
Jaya Kawale, Hung H. Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla
Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems. However, the problem of finding an optimal trade-off between exploration and exploitation (otherwise known as the bandit problem), a crucial problem in collaborative filtering from cold-start, has not been previously addressed.In this paper, we present a novel algorithm for online MF recommendation that automatically combines finding the most relevantitems with exploring new or less-recommended items.Our approach, called Particle Thompson Sampling for Matrix-Factorization, is based on the general Thompson sampling framework, but augmented with a novel efficient online Bayesian probabilistic matrix factorization method based on the Rao-Blackwellized particle filter.Extensive experiments in collaborative filtering using several real-world datasets demonstrate that our proposed algorithm significantly outperforms the current state-of-the-arts.

328. Time-Sensitive Recommendation From Recurrent User Activities [PDF] 返回目录
NeurIPS 2015.
Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song
By making personalized suggestions, a recommender system is playing a crucial role in improving the engagement of users in modern web-services. However, most recommendation algorithms do not explicitly take into account the temporal behavior and the recurrent activities of users. Two central but less explored questions are how to recommend the most desirable item \emph{at the right moment}, and how to predict \emph{the next returning time} of a user to a service. To address these questions, we propose a novel framework which connects self-exciting point processes and low-rank models to capture the recurrent temporal patterns in a large collection of user-item consumption pairs. We show that the parameters of the model can be estimated via a convex optimization, and furthermore, we develop an efficient algorithm that maintains $O(1 / \epsilon)$ convergence rate, scales up to problems with millions of user-item pairs and thousands of millions of temporal events. Compared to other state-of-the-arts in both synthetic and real datasets, our model achieves superb predictive performance in the two time-sensitive recommendation questions. Finally, we point out that our formulation can incorporate other extra context information of users, such as profile, textual and spatial features.

329. TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation [PDF] 返回目录
AAAI 2014. Main Track: AI and the Web
Yang Bao, Hui Fang, Jie Zhang
Although users' preference is semantically reflected in the free-form review texts, this wealth of information was not fully exploited for learning recommender models. Specifically, almost all existing recommendation algorithms only exploit rating scores in order to find users' preference, but ignore the review texts accompanied with rating information. In this paper, we propose a novel matrix factorization model (called TopicMF) which simultaneously considers the ratings and accompanied review texts. Experimental results on 22 real-world datasets show the superiority of our model over the state-of-the-art models, demonstrating its effectiveness for recommendation tasks.

330. Context-Aware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems [PDF] 返回目录
AAAI 2014. Main Track: AI and the Web
Chaochao Chen, Xiaolin Zheng, Yan Wang, Fuxing Hong, Zhen Lin
Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we propose a novel context-aware hierarchical Bayesian method. First, we propose the use of spectral clustering for user-item subgrouping, so that users and items in similar contexts are grouped. We then propose a novel hierarchical Bayesian model that can make predictions for each user-item subgroup, our model incorporate not only topic modeling to mine item content but also social matrix factorization to handle ratings and social relationships. Experiments on an Epinions dataset show that our method significantly improves recommendation performance compared with six categories of state-of-the-art recommendation methods in terms of both prediction accuracy and recall. We have also conducted experiments to study the extent to which ratings, contexts, social relationships, and item contents contribute to recommendation performance in terms of prediction accuracy and recall.

331. Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation [PDF] 返回目录
AAAI 2014. Main Track: AI and the Web
Hui Fang, Yang Bao, Jie Zhang
Trust has been used to replace or complement rating-based similarity in recommender systems, to improve the accuracy of rating prediction. However, people trusting each other may not always share similar preferences. In this paper, we try to fill in this gap by decomposing the original single-aspect trust information into four general trust aspects, i.e. benevolence, integrity, competence, and predictability, and further employing the support vector regression technique to incorporate them into the probabilistic matrix factorization model for rating prediction in recommender systems. Experimental results on four datasets demonstrate the superiority of our method over the state-of-the-art approaches.

332. Combining Heterogenous Social and Geographical Information for Event Recommendation [PDF] 返回目录
AAAI 2014. Main Track: AI and the Web
Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo, Binxing Fang
With the rapid growth of event-based social networks (EBSNs) like Meetup, the demand for event recommendation becomes increasingly urgent. In EBSNs, event recommendation plays a central role in recommending the most relevant events to users who are likely to participate in. Different from traditional recommendation problems, event recommendation encounters three new types of information, i.e., heterogenous online+offline social relationships, geographical features of events and implicit rating data from users. Yet combining the three types of data for offline event recommendation has not been considered. Therefore, we present a Bayesian latent factor model that can unify these data for event recommendation. Experimental results on real-world data sets show the performance of our method.

333. Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems [PDF] 返回目录
AAAI 2014. Main Track: AI and the Web
Beidou Wang, Martin Ester, Jiajun Bu, Deng Cai
Social explanation, the statement with the form of "A and B also like the item", is widely used in almost all the major recommender systems in the web and effectively improves the persuasiveness of the recommendation results by convincing more users to try. This paper presents the first algorithm to generate the most persuasive social explanation by recommending the optimal set of users to be put in the explanation. New challenges like modeling persuasiveness of multiple users, different types of users in social network, sparsity of likes, are discussed in depth and solved in our algorithm. The extensive evaluation demonstrates the advantage of our proposed algorithm compared with traditional methods.

334. Recommendation by Mining Multiple User Behaviors with Group Sparsity [PDF] 返回目录
AAAI 2014. Main Track: AI and the Web
Ting Yuan, Jian Cheng, Xi Zhang, Shuang Qiu, Hanqing Lu
Recently, some recommendation methods try to improvethe prediction results by integrating informationfrom user’s multiple types of behaviors. How to modelthe dependence and independence between differentbehaviors is critical for them. In this paper, we proposea novel recommendation model, the Group-Sparse MatrixFactorization (GSMF), which factorizes the ratingmatrices for multiple behaviors into the user and itemlatent factor space with group sparsity regularization.It can (1) select out the different subsets of latent factorsfor different behaviors, addressing that users’ decisionson different behaviors are determined by differentsets of factors;(2) model the dependence and independencebetween behaviors by learning the sharedand private factors for multiple behaviors automatically; (3) allow the shared factors between different behaviorsto be different, instead of all the behaviors sharingthe same set of factors. Experiments on the real-world dataset demonstrate that our model can integrate users’multiple types of behaviors into recommendation better,compared with other state-of-the-arts.

335. Signals in the Silence: Models of Implicit Feedback in a Recommendation System for Crowdsourcing [PDF] 返回目录
AAAI 2014. Main Track: Human-Computation and Crowd Sourcing
Christopher H. Lin, Ece Kamar, Eric Horvitz
We exploit the absence of signals as informative observations in the context of providing task recommendations in crowdsourcing. Workers on crowdsourcing platforms do not provide explicit ratings about tasks. We present methods that enable a system to leverage implicit signals about task preferences. These signals include types of tasks that have been available and have been displayed, and the number of tasks workers select and complete. In contrast to previous work, we present a general model that can represent both positive and negative implicit signals. We introduce algorithms that can learn these models without exceeding the computational complexity of existing approaches. Finally, using data from a high-throughput crowdsourcing platform, we show that reasoning about both positive and negative implicit feedback can improve the quality of task recommendations.

336. Deep Modeling of Group Preferences for Group-Based Recommendation [PDF] 返回目录
AAAI 2014. Main Track: Novel Machine Learning Algorithms
Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, Wei Cao
Nowadays, most recommender systems (RSs) mainly aim to suggest appropriate items for individuals. Due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group RS (GRS). However, most existing methods used by GRS make recommendations through aggregating individual ratings or individual predictive results rather than considering the collective features that govern user choices made within a group. As a result, such methods are heavily sensitive to data, hence they often fail to learn group preferences when the data are slightly inconsistent with predefined aggregation assumptions. To this end, we devise a novel GRS approach which accommodates both individual choices and group decisions in a joint model. More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines. With such a deep model, we can use high-level features, which are induced from lower-level features, to represent group preference so as to relieve the vulnerability of data. Finally, the experiments conducted on a real-world dataset prove the superiority of our deep model over other state-of-the-art methods.

337. Evaluation and Deployment of a People-to-People Recommender in Online Dating [PDF] 返回目录
AAAI 2014. Deployed Application Case Studies
Alfred Krzywicki, Wayne Wobcke, Yang Sok Kim, Xiongcai Cai, Michael Bain, Paul Compton, Ashesh Mahidadia
This paper reports on the successful deployment of a people-to-people recommender system in a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that key metrics generally hold their value or show an increase compared to the trial results, and that the recommender system delivered its projected benefits.

338. Deploying CommunityCommands: A Software Command Recommender System Case Study [PDF] 返回目录
AAAI 2014. Deployed Application Case Studies
Wei Li, Justin Matejka, Tovi Grossman, George W. Fitzmaurice
In 2009 we presented the idea of using collaborative filtering within a complex software application to help users learn new and relevant commands (Matejka et al. 2009). This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a four-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. CommunityCommands was made available as a publically available plug-in download for Autodesk’s flagship software application AutoCAD. During a one-year period, the recommender system was used by more than 1100 AutoCAD users. In this paper, we present our system usage data and payoff. We also provide an in-depth discussion of the challenges and design issues associated with developing and deploying the front end AutoCAD plug-in and its back end system. This includes a detailed description of the issues surrounding cold start and privacy. We also discuss how our practical system architecture was designed to leverage Autodesk’s existing Customer Involvement Program (CIP) data to deliver in-product contextual recommendations to end-users. Our work sets important groundwork for the future development of recommender systems within the domain of end-user software learning assistance.

339. Event Recommendation in Event-Based Social Networks [PDF] 返回目录
AAAI 2014. Student Abstracts
Zhi Qiao, Peng Zhang, Chuan Zhou, Yanan Cao, Li Guo, Yanchuan Zhang
With the rapid growth of event-based social networks, the demand of event recommendation becomes increasingly important. Different from classic recommendation problems, event recommendation generally faces the problems of heterogenous online and offline social relationships among users and implicit feedback data. In this paper, we present a baysian probability model that can fully unleash the power of heterogenous social relations and efficiently tackle with implicit feedback characteristic for event recommendation. Experimental results on several real-world datasets demonstrate the utility of our method.

340. Citation Resolution: A method for evaluating context-based citation recommendation systems [PDF] 返回目录
ACL 2014. Short Papers
Daniel Duma, Ewan Klein

341. Time-aware Personalized Hashtag Recommendation on Social Media [PDF] 返回目录
COLING 2014.
Qi Zhang, Yeyun Gong, Xuyang Sun, Xuanjing Huang

342. Collaborative Topic Regression with Multiple Graphs Factorization for Recommendation in Social Media [PDF] 返回目录
COLING 2014.
Qing Zhang, Houfeng Wang

343. Enforcing Topic Diversity in a Document Recommender for Conversations [PDF] 返回目录
COLING 2014.
Maryam Habibi, Andrei Popescu-Belis

344. Controlling privacy in recommender systems [PDF] 返回目录
NeurIPS 2014.
Yu Xin, Tommi Jaakkola
Recommender systems involve an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. In this paper, we explore a two-tiered notion of privacy where there is a small set of ``public'' users who are willing to share their preferences openly, and a large set of ``private'' users who require privacy guarantees. We show theoretically and demonstrate empirically that a moderate number of public users with no access to private user information already suffices for reasonable accuracy. Moreover, we introduce a new privacy concept for gleaning relational information from private users while maintaining a first order deniability. We demonstrate gains from controlled access to private user preferences.

345. Content-based recommendations with Poisson factorization [PDF] 返回目录
NeurIPS 2014.
Prem K. Gopalan, Laurent Charlin, David Blei
We develop collaborative topic Poisson factorization (CTPF), a generative model of articles and reader preferences. CTPF can be used to build recommender systems by learning from reader histories and content to recommend personalized articles of interest. In detail, CTPF models both reader behavior and article texts with Poisson distributions, connecting the latent topics that represent the texts with the latent preferences that represent the readers. This provides better recommendations than competing methods and gives an interpretable latent space for understanding patterns of readership. Further, we exploit stochastic variational inference to model massive real-world datasets. For example, we can fit CPTF to the full arXiv usage dataset, which contains over 43 million ratings and 42 million word counts, within a day. We demonstrate empirically that our model outperforms several baselines, including the previous state-of-the-art approach.

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