RBPR: Role-based Bayesian Personalized Ranking for Heterogeneous One-Class Collaborative Filtering

Heterogeneous one-class collaborative filtering (HOCCF) is a recently studied important recommendation problem, which consists of different types of users’ one-class feedback such as browses and purchases. In HOCCF, we aim to fully exploit the heterogenous feedback and learn users’ preferences so as to make a personalized and ranking-oriented recommendation for each user. For HOCCF, we can apply existing solutions for OCCF with purchases only such as Bayesian personalized ranking (BPR) or make use of both browses and purchases such as transfer via joint similarity learning (TJSL). However, BPR may be not very accurate due to the ignorance of browses, and TJSL may be not very efficient due to the mechanism of joint similarity learning and base model aggregation. In this paper, we propose a novel perspective for the different types of one-class feedback via users’ different roles, i.e., browser and purchaser. Specifically, we design a two-stage role-based preference learning framework, i.e., role-based Bayesian personalized ranking (RBPR). In RBPR, we first digest the combined one-class feedback as a browser to find the candidate items that a user will browse, and then we exploit the purchase feedback to refine the candidate list as a purchaser. Empirical results on five public datasets show that our RBPR is an efficient and accurate recommendation algorithm for HOCCF as compared with the state-of-the-art methods such as BPR and TJSL.