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This paper presents a solution to the challenges faced by contrastivelearning in sequential recommendation systems. In particular, it addresses theissue of false negative, which limits the effectiveness of recommendationalgorithms. By introducing an advanced approach to contrastive learning, theproposed method improves the quality of item embeddings and mitigates theproblem of falsely categorizing similar instances as dissimilar. Experimentalresults demonstrate performance enhancements compared to existing systems. Theflexibility and applicability of the proposed approach across variousrecommendation scenarios further highlight its value in enhancing sequentialrecommendation systems.

AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation