Self-supervised contrastive learning for implicit collaborative filtering

Abstract

Recommendation systems are a critical application of artificial intelligence (AI), driving personalized user experiences across various platforms. Recent advancements in contrastive learning-based recommendation algorithms have led to significant progress in self-supervised recommendation. A key method in this field is Bayesian Personalized Ranking (BPR), which has become a dominant approach for implicit collaborative filtering. However, the challenge of false-positive and false-negative examples in implicit feedback continues to hinder accurate preference learning. In this study, we introduce an efficient self-supervised contrastive learning framework that enhances the supervisory signal by incorporating positive feature augmentation and negative label augmentation. Our theoretical analysis reveals that this approach is equivalent to maximizing the likelihood estimation with latent variables representing user interest centers. Additionally, we present a novel negative label augmentation technique that selects unlabeled examples based on their relative ranking positions, enabling efficient augmentation with constant time complexity. Validation on the MovieLens-100k, MovieLens-1M, Yahoo!-R3, Yelp2018, and Gowalla datasets demonstrates that our method achieves over a 5% improvement in precision compared to the widely used BPR optimization objective, while maintaining comparable runtime efficiency.