Ranking-based contrastive loss for recommendation systems

Abstract

The recommendation system is fundamental technology of the internet industry intended to solve the information overload problem in the big data era. Top-k recommendation is an important task in this field. It generally functions through the comparison of positive pairs and negative pairs based on Bayesian personalized ranking (BPR) loss. We find that the contrastive loss (CL) function used in contrastive learning is well-suited for top-k recommendation. However, there are two problems in the existing loss functions. First, all samples are treated the same, and hard samples are not considered. Second, all nonpositive samples are considered negative samples, which ignores the fact that they are unlabelled data containing items that users may like. Moreover, in our experiments, we find that when items are sorted by their similarities to the user, many negative items (or samples) appear before the positive items. We regard these negative items as hard samples and those at the top as potentially positive samples due to their high level of similarities with users. Therefore, we propose a ranking-based contrastive loss (RCL) function to exploit both hard samples and potentially positive samples. Experimental results demonstrate the effectiveness, broad applicability, and high training efficiency of the proposed RCL function. The code and data are available at https://github.com/haotangxjtu/RCL.