Exploiting Group Pairwise Preference Influences for Recommendations

Recommender systems always recommend items to a user based on predicted ratings. However, due to biases of different users, it is not easy to know a user’s preference through the predicted ratings. This paper defines a user preference relationship based on the user’s ratings to improve the recommendation accuracy. By considering group information, we extend the preference relationship to form four types of correlations including (user, item), (user group, item), (user, item group), and (user group, item group). And then, this paper exploits pair-wise comparisons between two items or two group of items for a singer user or a group of users. The gradient descent algorithm is used to learn latent factors on partial orders to make recommendations. Experimental results show the effectiveness of the proposed method.

[1]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

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

[3]  Yiyu Yao,et al.  A UNIFIED FRAMEWORK OF TARGETED MARKETING USING CUSTOMER PREFERENCES , 2014, Comput. Intell..

[4]  Sattar Hashemi,et al.  Incorporating Hierarchical Information into the Matrix Factorization Model for Collaborative Filtering , 2012, ACIIDS.

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

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

[7]  Svetha Venkatesh,et al.  Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach , 2016, Knowledge and Information Systems.

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

[9]  Enhong Chen,et al.  Learning recency based comparative choice towards point-of-interest recommendation , 2015, Expert Syst. Appl..

[10]  Li Chen,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering , 2022 .

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

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

[13]  S. French.  Decision Theory: An Introduction to the Mathematics of Rationality , 1986 .