Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering

One-class collaborative filtering or collaborative ranking with implicit feedback has been steadily receiving more attention, mostly due to the "one-class" characteristics of data in various services, e.g., "like" in Facebook and "bought" in Amazon. Previous works for solving this problem include pointwise regression methods based on absolute rating assumptions and pairwise ranking methods with relative score assumptions, where the latter was empirically found performing much better because it models users' ranking-related preferences more directly. However, the two fundamental assumptions made in the pairwise ranking methods, (1) individual pairwise preference over two items and (2) independence between two users, may not always hold. As a response, we propose a new and improved assumption, group Bayesian personalized ranking (GBPR), via introducing richer interactions among users. In particular, we introduce group preference, to relax the aforementioned individual and independence assumptions. We then design a novel algorithm correspondingly, which can recommend items more accurately as shown by various ranking-oriented evaluation metrics on four real-world datasets in our experiments.

[1]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[2]  Xuan Li,et al.  User Graph Regularized Pairwise Matrix Factorization for Item Recommendation , 2011, ADMA.

[3]  Steffen Rendle,et al.  Factorization Machines with libFM , 2012, TIST.

[4]  Alexander J. Smola,et al.  Maximum Margin Matrix Factorization for Collaborative Ranking , 2007 .

[5]  Min Zhao,et al.  Social temporal collaborative ranking for context aware movie recommendation , 2013, TIST.

[6]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[7]  David R. Karger,et al.  Less is More Probabilistic Models for Retrieving Fewer Relevant Documents , 2006 .

[8]  Vanja Josifovski,et al.  Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior , 2012, Proc. VLDB Endow..

[9]  Qiang Yang,et al.  Transfer Learning in Collaborative Filtering with Uncertain Ratings , 2012, AAAI.

[10]  Qiang Yang,et al.  One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[11]  Shuang-Hong Yang,et al.  Collaborative competitive filtering: learning recommender using context of user choice , 2011, SIGIR.

[12]  Cong Yu,et al.  Group Recommendation: Semantics and Efficiency , 2009, Proc. VLDB Endow..

[13]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[14]  Lars Schmidt-Thieme,et al.  Pairwise interaction tensor factorization for personalized tag recommendation , 2010, WSDM '10.

[15]  Li Chen,et al.  Users' eye gaze pattern in organization-based recommender interfaces , 2011, IUI '11.

[16]  Lars Schmidt-Thieme,et al.  Factorizing personalized Markov chains for next-basket recommendation , 2010, WWW '10.

[17]  Tsau Young Lin,et al.  Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November - 2 December 2001, San Jose, California, USA , 2001 .

[18]  Aleksandra Mojsilovic,et al.  A Family of Non-negative Matrix Factorizations for One-Class Collaborative Filtering Problems , 2009 .

[19]  Li Chen,et al.  CoFiSet: Collaborative Filtering via Learning Pairwise Preferences over Item-sets , 2013, SDM.

[20]  Lars Schmidt-Thieme,et al.  Multi-relational matrix factorization using bayesian personalized ranking for social network data , 2012, WSDM '12.

[21]  David M. Nichols,et al.  Implicit Rating and Filtering , 1998 .