GPR-OPT: A Practical Gaussian optimization criterion for implicit recommender systems

Abstract

Implicit recommendation refers to the users’ feedback on items derived from their interactions with items, i.e., clicks, and purchases. The methods in the implicit recommendation scenario usually regard all the adopted items as their favorites and indiscriminately assign a uniform confidence weight of their preference toward all the adopted items. In practice, however, a user’s preferences toward different items vary a lot. Treating them equally in existing implicit feedback recommender systems may limit the capacity of learning algorithms. To address this problem, we propose a novel Gaussian Personalized Recommendation OPTimization criterion (GPR-OPT), and our aim is to make the unknown preference confidence of users toward their adopted items in implicit feedback recommendation be learnable, so as to improve the accuracy of implicit recommender systems. In particular, we assume the user’s interests in items follow Gaussian distributions. By maximizing the posterior probability of items derived from the Gaussian distribution of user features, GPR-OPT is able to self-adaptively learn the confidence of users’ preferences from the implicit user–item interactions. We conduct extensive experiments on three real-world datasets, i.e., Movielens 1M, Amazon Book, and Yelp, which show an average of 11.64% improvements over different kinds of collaborative filtering algorithms. GPR-OPT is a generic optimization criterion and can be easily integrated into most existing collaborative filtering recommendation models, leading to a great impact on implicit recommender systems.