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Author

Rui Zhang

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Open access Aug 2026

HGAT-Rec: a hierarchical graph attention framework for cross-domain e-commerce recommendation with heterogeneity-aware contrastive alignment

HGAT-Rec is proposed, which incorporates a heterogeneity-aware contrastive learning (HCL) objective that grounds view construction and sample selection in the typed relational structure of a cross-domain heterogeneous graph: type-stratified edge dropout preserves high-signal interaction channels proportionally to their attention weight.

Rui Zhang, YaTing Zhao, FengBo Wang et al. · 0 citations

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