Social Collaborative Viewpoint Regression with Explainable Recommendations

A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To this end, we use so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations. In addition, such viewpoints can be used as explanations. We apply a Gibbs EM sampler to infer posterior distributions of sCVR. Experiments conducted on three large benchmark datasets show the effectiveness of our proposed method for predicting item ratings and for generating explanations.

[1]  Chong Wang,et al.  Collaborative topic modeling for recommending scientific articles , 2011, KDD.

[2]  Martin Ester,et al.  A matrix factorization technique with trust propagation for recommendation in social networks , 2010, RecSys '10.

[3]  Guokun Lai,et al.  Explicit factor models for explainable recommendation based on phrase-level sentiment analysis , 2014, SIGIR.

[4]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[5]  Raymond J. Mooney,et al.  Explaining Recommendations: Satisfaction vs. Promotion , 2005 .

[6]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[7]  Ting Liu,et al.  Document Modeling with Gated Recurrent Neural Network for Sentiment Classification , 2015, EMNLP.

[8]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[9]  Jiayu Zhou,et al.  Who, What, When, and Where: Multi-Dimensional Collaborative Recommendations Using Tensor Factorization on Sparse User-Generated Data , 2015, WWW.

[10]  Ioannis Konstas,et al.  On social networks and collaborative recommendation , 2009, SIGIR.

[11]  Judith Masthoff,et al.  Designing and Evaluating Explanations for Recommender Systems , 2011, Recommender Systems Handbook.

[12]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[13]  Wai Lam,et al.  Collaborative Filtering Incorporating Review Text and Co-clusters of Hidden User Communities and Item Groups , 2014, CIKM.

[14]  M. de Rijke,et al.  Summarizing Contrastive Themes via Hierarchical Non-Parametric Processes , 2015, SIGIR.

[15]  Hanna M. Wallach,et al.  Topic modeling: beyond bag-of-words , 2006, ICML.

[16]  Alexander J. Smola,et al.  Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) , 2014, KDD.

[17]  Ke Xu,et al.  MoodLens: an emoticon-based sentiment analysis system for chinese tweets , 2012, KDD.

[18]  Tao Chen,et al.  TriRank: Review-aware Explainable Recommendation by Modeling Aspects , 2015, CIKM.

[19]  Martha Larson,et al.  CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering , 2012, RecSys.

[20]  Yong Yu,et al.  Collaborative personalized tweet recommendation , 2012, SIGIR '12.

[21]  Ting Liu,et al.  User Modeling with Neural Network for Review Rating Prediction , 2015, IJCAI.

[22]  Michael R. Lyu,et al.  Learning to recommend with social trust ensemble , 2009, SIGIR.

[23]  Dan Cosley,et al.  Do social explanations work?: studying and modeling the effects of social explanations in recommender systems , 2013, WWW.

[24]  Tie-Yan Liu,et al.  Listwise Collaborative Filtering , 2015, SIGIR.

[25]  Shi Feng,et al.  Localized matrix factorization for recommendation based on matrix block diagonal forms , 2013, WWW.

[26]  Alexander J. Smola,et al.  Like like alike: joint friendship and interest propagation in social networks , 2011, WWW.

[27]  T. Minka.  Estimating a Dirichlet distribution , 2012 .

[28]  Lora Aroyo,et al.  Time-aware Multi-Viewpoint Summarization of Multilingual Social Text Streams , 2016, CIKM.

[29]  M. de Rijke,et al.  Ad Hoc Monitoring of Vocabulary Shifts over Time , 2015, CIKM.

[30]  Philip S. Yu,et al.  A holistic lexicon-based approach to opinion mining , 2008, WSDM '08.

[31]  Ming Zhou,et al.  Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification , 2014, ACL.

[32]  Gary Marchionini,et al.  Synthesis Lectures on Information Concepts, Retrieval, and Services , 2009 .

[33]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[34]  Alan Hanjalic,et al.  List-wise learning to rank with matrix factorization for collaborative filtering , 2010, RecSys '10.

[35]  Hao Yu,et al.  Structure-Aware Review Mining and Summarization , 2010, COLING.

[36]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[37]  Mike Thelwall,et al.  Sentiment strength detection for the social web , 2012, J. Assoc. Inf. Sci. Technol..

[38]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Mao Ye,et al.  Exploring social influence for recommendation: a generative model approach , 2012, SIGIR '12.

[40]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[41]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[42]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[43]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[44]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[45]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[46]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[47]  Alexander J. Smola,et al.  CoBaFi: collaborative bayesian filtering , 2014, WWW.

[48]  John Riedl,et al.  Tagsplanations: explaining recommendations using tags , 2009, IUI.

[49]  M. de Rijke,et al.  Click Models for Web Search , 2015, Click Models for Web Search.

[50]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[51]  Zhen Lin,et al.  Context-Aware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems , 2014, AAAI.

[52]  Michael R. Lyu,et al.  Ratings meet reviews, a combined approach to recommend , 2014, RecSys '14.

[53]  Yehuda Koren,et al.  Improved Neighborhood-based Collaborative Filtering , 2007 .

[54]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[55]  Jiming Liu,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Social Collaborative Filtering by Trust , 2022 .