Soft Contrastive Learning for Implicit Feedback Recommendations

Collaborative filtering (CF) plays a crucial role in the development of recommendations. Most CF research focuses on implicit feedback due to its accessibility, but deriving user preferences from such feedback is challenging given the inherent noise in interactions. Existing works primarily employ unobserved interactions as negative samples, leading to a critical noisy-label problem. In this study, we propose SCLRec (Soft Contrastive Learning for Recommendations), a novel method to alleviate the noise issue in implicit recommendations. To this end, we first construct a similarity matrix based on user and item embeddings along with item popularity information. Subsequently, to leverage information from nearby samples, we employ entropy optimal transport to obtain the matching matrix from the similarity matrix. The matching matrix provides additional supervisory signals that uncover matching relationships of unobserved user-item interactions, thereby mitigating the noise issue. Finally, we treat the matching matrix as soft targets, and use them to train the model via contrastive learning loss. Thus, we term it soft contrastive learning, which combines the denoising capability of soft targets with the representational strength of contrastive learning to enhance implicit recommendations. Extensive experiments on three public datasets demonstrate that SCLRec achieves consistent performance improvements compared to state-of-the-art CF methods.