Weighted item ranking for pairwise matrix factorization

Recommendation systems employed on the Internet aim to serve users by recommending items which will likely be of interest to them. The recommendation problem could be cast as either a rating estimation problem which aims to predict as accurately as possible for a user the rating values of items which are yet unrated by that user, or as a ranking problem which aims to find the top-k ranked items that would be of most interest to a user, which s/he has not ranked yet. In contexts where explicit item ratings of other users may not be available, the ranking prediction could be more important than the rating prediction. Most of the existing ranking-based prediction approaches consider items as having equal weights which is not always the case. Different weights of items could be regarded as a reflection of items' importance, or desirability, to users. In this paper, we propose to integrate variable item weights with a ranking-based matrix factorization model, where learning is driven by Bayesian Personalized Ranking (BPR). Two ranking-based models utilizing different-weight learning methods are proposed and the performance of both models is confirmed as being better than the standard BPR method.

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

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

[3]  Tong Zhao,et al.  Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering , 2014, CIKM.

[4]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[5]  Ivan Ganchev,et al.  Weighted matrix factorization with Bayesian personalized ranking , 2017, 2017 Computing Conference.

[6]  Neil Yorke-Smith,et al.  A Novel Recommendation Model Regularized with User Trust and Item Ratings , 2016, IEEE Transactions on Knowledge and Data Engineering.

[7]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[8]  Tie-Yan Liu,et al.  A Theoretical Analysis of NDCG Type Ranking Measures , 2013, COLT.

[9]  Jin-guang Sun,et al.  A Neighborhood-based Matrix Factorization Technique for Recommendation , 2015 .

[10]  Marco Gori,et al.  ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines , 2007, IJCAI.

[11]  J. Golbeck.  Filmtrust: movie recommendations from semantic web-based social networks , 2006, CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006..

[12]  Zhongfu Wu,et al.  Personalisation in web computing and informatics: Theories, techniques, applications, and future research , 2010, Inf. Syst. Frontiers.

[13]  Zhongfu Wu,et al.  Userrank for item-based collaborative filtering recommendation , 2011, Inf. Process. Lett..

[14]  Alexander J. Smola,et al.  Parallelized Stochastic Gradient Descent , 2010, NIPS.

[15]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

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

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

[18]  Congfu Xu,et al.  Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks , 2015, Knowl. Based Syst..

[19]  Ivan Ganchev,et al.  A trust-enriched approach for item-based collaborative filtering recommendations , 2016, 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP).

[20]  Yuanzhi Li,et al.  A Theoretical Analysis of NDCG Ranking Measures , 2013 .

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

[22]  Xiaodan Hu,et al.  A Novel Frequent Itemsets Finding Algorithm Based on Variant Support Value , 2008 .

[23]  Zibin Zheng,et al.  Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization , 2013, IEEE Transactions on Services Computing.