Asymmetric Bayesian personalized ranking for one-class collaborative filtering

Shan Ouyang, Lin Li, Weike Pan, Zhong Ming

Abstract: In this paper, we propose a novel preference assumption for modeling users' one-class feedback such as "thumb up" in an important recommendation problem called one-class collaborative filtering (OCCF). Specifically, we address a fundamental limitation of a recent symmetric pairwise preference assumption and propose a novel and first asymmetric one, which is able to make the preferences of different users more comparable. With the proposed asymmetric pairwise preference assumption, we further design a novel recommendation algorithm called asymmetric Bayesian personalized ranking (ABPR). Extensive empirical studies on two large and public datasets show that our ABPR performs significantly better than several state-of-the-art recommendation methods with either pointwise preference assumption or pairwise preference assumption.