Group Bayesian personalized ranking with rich interactions for one-class collaborative filtering

Both researchers and practitioners in the field of collaborative filtering have shown keen interest to user behaviors of the “one-class” feedback form such as transactions in e-commerce and “likes” in social networks. This recommendation problem is termed as one-class collaborative filtering (OCCF). In most of the previous work, a pairwise preference assumption called Bayesian personalized ranking (BPR) was empirically proved to be able to exploit such one-class data well. In one of the most recent work, an upgraded model called group preference based BPR (GBPR) leverages the group preference and obtains better performance. In this paper, we go one step beyond GBPR, and propose a new and generic assumption, i.e., group Bayesian personalized ranking with rich interactions (GBPR⁺). In our GBPR⁺, we adopt a set of items instead of one single item as used in GBPR, which is expected to introduce rich interactions. GBPR is a special case of our GPBR⁺ when the item set contains only one single item. We study the empirical performance of our GBPR⁺ with several state-of-the-art methods on four real-world datasets, and find that our GPBR⁺ can generate more accurate recommendations.