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What is Sequential Recommendation via Contrastive Learning about?

This paper introduces ContraRec, a novel framework for sequential recommendation that integrates two types of contrastive learning tasks: context-target contrast and context-context contrast. By leveraging self-supervised signals from user interaction sequences, ContraRec enhances the representation learning of sequential recommendation models, leading to improved performance over state-of-the-art methods. Extensive experiments demonstrate that ContraRec is effective, particularly in scenarios with limited

Author

yfwang371

Language

EN