SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender System
Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Enhong Chen, Hui Xiong
Abstract
The recent development of recommender systems has a focus on collaborative ranking, which provides users with a sorted list rather than rating prediction. The sorted item lists can more directly reflect the preferences for users and usually perform better than rating prediction in practice. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” and unobserved data due to the precondition of the entire list permutation. To this end, in this article, we propose a novel setwise Bayesian approach for collaborative ranking, namely, SetRank, to inherently accommodate the characteristics of user feedback in recommender systems. SetRank aims to maximize the posterior probability of novel setwise preference structures and three implementations for SetRank are presented. We also theoretically prove that the bound of excess risk in SetRank can be proportional to \(\sqrt {M/N}\) , where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.