Two of a kind or the ratings game? Adaptive pairwise preferences and latent factor models

Latent factor models have become a workhorse for a large number of recommender systems. While these systems are built using ratings data, which is typically assumed static, the ability to incorporate different kinds of subsequent user feedback is an important asset. For instance, the user might want to provide additional information to the system in order to improve his personal recommendations. To this end, we examine a novel scheme for efficiently learning (or refining) user parameters from such feedback. We propose a scheme where users are presented with a sequence of pairwise preference questions: “Do you prefer item A over B?” User parameters are updated based on their response, and subsequent questions are chosen adaptively after incorporating the feedback. We operate in a Bayesian framework and the choice of questions is based on an information gain criterion. We validate the scheme on the Netflix movie ratings data set and a proprietary television viewership data set. A user study and automated experiments validate our findings.

[1]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[2]  A. Elo.  The rating of chessplayers, past and present , 1978 .

[3]  Yiming Yang,et al.  Personalized active learning for collaborative filtering , 2008, SIGIR '08.

[4]  Luo Si,et al.  A Bayesian Approach toward Active Learning for Collaborative Filtering , 2004, UAI.

[5]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[6]  Craig Boutilier,et al.  Active Collaborative Filtering , 2002, UAI.

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

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

[9]  Tom Minka,et al.  TrueSkillTM: A Bayesian Skill Rating System , 2006, NIPS.

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

[11]  Min Zhao,et al.  Probabilistic latent preference analysis for collaborative filtering , 2009, CIKM.

[12]  Luis von Ahn,et al.  Matchin: eliciting user preferences with an online game , 2009, CHI.

[13]  Wei Chu,et al.  Information Services]: Web-based services , 2022 .

[14]  D. Mackay,et al.  Bayesian methods for adaptive models , 1992 .

[15]  Sreenivas Gollapudi,et al.  Ranking mechanisms in twitter-like forums , 2010, WSDM '10.

[16]  Yuan Qi,et al.  Predictive automatic relevance determination by expectation propagation , 2004, ICML.

[17]  Deepak Agarwal,et al.  Regression-based latent factor models , 2009, KDD.