CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation

CAST addresses a core challenge in sequential recommendation systems: distinguishing genuine complementary product relationships from spurious correlations driven by popularity bias. The approach models fine-grained item semantics rather than relying on coarse co-purchase statistics, enabling more accurate next-item prediction.

MentionsCAST

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