A multi-attribute probabilistic matrix factorization model for personalized recommendation

AbstractRecommendation systems can interpret personal preferences and recommend the most relevant choices to the benefit of countless users. Attempts to improve the performance of recommendation systems have hence been the focus of much research in an era of information explosion. As users would like to ask about shopping information with their friend in real life and plentiful information concerning items can help to improve the recommendation accuracy, traditional work on recommending based on users’ social relationships or the content of item tagged by users fails as recommending process relies on mining a user’s historical information as much as possible. This paper proposes a new recommending model incorporating the social relationship and content information of items (SC) based on probabilistic matrix factorization named SC-PMF (Probabilistic Matrix Factorization with Social relationship and Content of items). Meanwhile, we take full advantage of the scalability of probabilistic matrix factorization, which helps to overcome the often encountered problem of data sparsity. Experiments demonstrate that SC-PMF is scalable and outperforms several baselines (PMF, LDA, CTR, SocialMF) for recommending.

[1]  Ryan P. Adams,et al.  Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes , 2010, UAI.

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

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

[4]  Gediminas Adomavicius,et al.  Context-aware recommender systems , 2008, RecSys '08.

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

[6]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

[7]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[8]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[9]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

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

[11]  Xiao-Dong Li,et al.  Comparison Study of Internet Recommendation System: Comparison Study of Internet Recommendation System , 2009 .

[12]  Chun-Yi Wu,et al.  Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system , 2011, Expert Syst. Appl..

[13]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

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

[15]  Arindam Banerjee,et al.  Generalized Probabilistic Matrix Factorizations for Collaborative Filtering , 2010, 2010 IEEE International Conference on Data Mining.

[16]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[17]  Ralf Krestel,et al.  Latent dirichlet allocation for tag recommendation , 2009, RecSys '09.

[18]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[19]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[20]  Charles Elkan,et al.  Expectation Maximization Algorithm , 2010, Encyclopedia of Machine Learning.

[21]  Rushed Kanawati,et al.  Link Prediction in Complex Networks , 2020, Cognitive Analytics.

[22]  Michael R. Lyu,et al.  Effective missing data prediction for collaborative filtering , 2007, SIGIR.

[23]  Hongyuan Zha,et al.  Exploring social annotations for information retrieval , 2008, WWW.

[24]  Rossano Schifanella,et al.  Friendship prediction and homophily in social media , 2012, TWEB.

[25]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[26]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[27]  Robin van Meteren.  Using Content-Based Filtering for Recommendation , 2000 .

[28]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[29]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

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

[31]  Jennifer Neville,et al.  Randomization tests for distinguishing social influence and homophily effects , 2010, WWW '10.

[32]  Yan Liu,et al.  Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems , 2012, ICML.

[33]  Ruslan Salakhutdinov,et al.  Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.

[34]  Wu-Jun Li,et al.  Collaborative Topic Regression with Social Regularization for Tag Recommendation , 2013, IJCAI.

[35]  Gregoris Mentzas,et al.  Using latent topics to enhance search and recommendation in Enterprise Social Software , 2012, Expert Syst. Appl..

[36]  James A. Hendler,et al.  Inferring binary trust relationships in Web-based social networks , 2006, TOIT.

[37]  Dominik Benz,et al.  The social bookmark and publication management system bibsonomy , 2010, The VLDB Journal.

[38]  Wu Xiao,et al.  Comparison Study of Internet Recommendation System , 2009 .