AI RESEARCH
ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation
arXiv CS.LG
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ArXi:2604.14114v1 Announce Type: cross Sequential recommendation has become increasingly prominent in both academia and industry, particularly in e-commerce. The primary goal is to extract user preferences from historical interaction sequences and predict items a user is likely to engage with next. Recent advances have leveraged contrastive learning and graph neural networks to learn expressive representations from interaction histories -- graphs capture relational structure between nodes, while ID-based representations encode item-specific information.