Semantic-enhanced Contrastive Learning for Session-based Recommendation

Session-based recommendation aims to predict the next clicked item based on the short-term behavior sequence of an anonymous user, which is a challenging task owing to data sparsity. Although contrastive learning has been used extensively to address this problem, existing methods generally consider all other sessions in the mini-batch as negative samples, leading to a limited negative sample space and a failure to distinguish real negative samples from false negative samples. Thus, Semantic-enhanced Contrastive Learning for Session-based Recommendation (SCLRec) is proposed in this study. Specifically, a queue is designed to store session samples, and a momentum encoder is used to ensure high consistency in the large-capacity sample space. Furthermore, a novel semantic-enhanced mechanism is devised to filter out false negative samples and increase the weights of high-confidence negative samples according to semantic similarity scores between sessions, effectively reducing noise and enhancing contrastive learning. Moreover, the gated attention unit is used as the encoder to obtain excellent performance and efficiency compared with traditional attention networks. Extensive experiments on three real-world public datasets demonstrate that the proposed method achieves state-of-the-art performance with a relatively low time complexity.