  This paper presents a solution to the challenges faced by contrastive
learning in sequential recommendation systems. In particular, it addresses the
issue of false negative, which limits the effectiveness of recommendation
algorithms. By introducing an advanced approach to contrastive learning, the
proposed method improves the quality of item embeddings and mitigates the
problem of falsely categorizing similar instances as dissimilar. Experimental
results demonstrate performance enhancements compared to existing systems. The
flexibility and applicability of the proposed approach across various
recommendation scenarios further highlight its value in enhancing sequential
recommendation systems.
