Heterogeneous Graph Dual-structure Optimization Based Attribute-aware for Recommendation

Abstract

Heterogeneous Graph Neural Networks(HGNNs) are widely regarded as an effective tool for modeling data with graph structures in recommendation. Current research lacks modeling of user attribute and project attribute distribution preferences, limiting graph structure optimization potential. In response to these challenges, we propose a novel Heterogeneous Graph Dual-structure Optimization based Attribute-aware for Recommendation systems (HDSAR). It captures users’ personalized preferences through attribute-aware enhancement and uses dual-structure optimization to improve recommendation performance. First, we design an attribute-aware enhancement module to significantly enhance the relevance of attributes between users and items. Second, we use attribute-aware signals to explicitly filter heterogeneous neighbor ranges to preserve high- quality structural neighborhoods. Then, we employ contrastive learning to enhance the consistency of attribute-aware signals and heterogeneous structures to implicitly optimize the structural learning. Experiments on two real-world datasets demonstrate that HDSAR’s recommendation performance surpasses that of state-of-the-art methods.