Abstract: Traditional GNNs(Graph Neural Networks), while capable of representing interactions between users and items, are typically limited to pairwise binary relationships, making it challenging to fully model high-order associations between users and items. To address this issue, a GR-HGAN(Global Relational-aware Hypergraph Attention Network for Recommender Systems) is proposed. First, GR-HGAN adopts a hypergraph structure that allows a single hyperedge to simultaneously connect multiple users and items, naturally capturing high-order associations in multi-party interactions. Then, GR-HGAN uses a hypergraph attention mechanism to focus on the relevance of different neighboring nodes to the target node, enhancing the influence of significant nodes in feature aggregation. Finally, by integrating a message-passing mechanism from both the global hypergraph and local subgraphs, it effectively learns high-order associations between nodes from a global perspective while incorporating critical information from local subgraphs, further improving the representation capability of node embeddings. Extensive experiments conducted on widely used datasets such as Yelp, MovieLens, and Amazon demonstrate that GR-HGAN outperforms state-of-the-art recommendation methods. 

Key words: recommender system, hypergraph neural network, hypergraph attention