RankMBPR: Rank Aware Mutual Bayesian Personalized Ranking for Item Recommendation
Previous works indicated that pairwise methods are state of the art approaches to fit users' taste from implicit feedback. In this paper, we argue that constructing item pairwise samples for a fixed user is insufficient, because taste differences between two users with respect to a same item can not be explicitly distinguished. Moreover, the rank position of positive items are not used as a metric to measure the learning magnitude in the next step. Therefore, we firstly define a confidence function to dynamically control the learning step size for updating model parameters. Sequently, we introduce a generic way to construct mutual pairwise loss from both users' and items' perspective. Instead of user oriented pairwise sampling strategy alone, we incorporate item pairwise samples into a popular pairwise learning framework, bayesian personalized ranking (BPR) and propose mutual bayesian personalized ranking (MBPR) method. In addition, a rank aware adaptively sampling strategy is proposed to come up with the final approach, called RankMBPR. Empirical studies are carried out on four real world datasets, and experimental results in several metrics demonstrate the efficiency and effectiveness of our proposed method, comparing with other baseline algorithms.
WAIM