MSBPR: A multi-pairwise preference and similarity based Bayesian personalized ranking method for recommendation

For addressing the "One-Class Collaborative Filtering"(OCCF) problem in recommendation systems, in which the obtained user information is all single-type positive feedback, the current mainstream methods are all based on the idea of pairwise preference learning. The Bayesian Personalized Ranking (BPR) method is a classical representative of such an idea. However, the assumption in BPR, that "users tend to prefer items that they have once interacted", may not always hold in reality. This is because for non-interacted items, a user may have different perspectives, such as potentially favorite, dislike, or something in between. For mitigating the above-mentioned issue, this paper proposes a Multi-pairwise preference and Similarity based BPR method, termed as MSBPR for brevity. MSBPR utilizes additional auxiliary feedback information to excavate and infer deeper-level user preferences. Subsequently, MSBPR borrows ideas from traditional item/user-based collaborative filtering methods to further divide non-interacted items from the angle of item/user, respectively. Afterwards, MSBPR constructs four preferences on top of the divided items, and accordingly builds up the multiple pairwise preference assumption. To optimize MSBPR, we derive an efficient learning algorithm based on the stochastic gradient descent algorithm. The computational complexity of MSBPR is also theoretically analyzed. Comprehensive experimental results demonstrate the effectiveness and efficiency of MSBPR over ten state-of-the-art methods on six benchmark real-world datasets.(c) 2022 Elsevier B.V. All rights reserved.