H2OGNN: Hypergraph-based heterophily-aware neural network for service recommendation

Service recommendation systems play a crucial role in delivering personalized user experiences across various domains. However, capturing the heterophily patterns and the multi-dimensional nature of user-service item interactions poses significant challenges. To address these challenges, we propose a service recommendation model named H 2 O GNN (Hypergraph-based Heterophily-Aware Neural Network for Service Recommendation), which incorporates three key components: (1) a hypergraph disentangled contrast module that explicitly separates homophily and heterophily signals within hyperedges, enabling more accurate feature representation; (2) a heterophily-aware attention mechanism that dynamically adjusts information propagation weights based on node feature differences, enhancing interaction efficiency between heterophilic nodes; and (3) a dynamic multi-interest learning module that disentangles users’ latent preferences into multiple interest vectors and activates relevant interests according to target service items, achieving fine-grained modeling of cross-category preferences. Extensive experiments on Steam, MovieLens, and Yelp datasets demonstrate the effectiveness of H 2 O GNN, with its Recall@20 metric outperforming the state-of-the-art baseline (HMGSR) by approximately 14.1% on Steam, 12.0% on MovieLens, and 13.9% on Yelp—and is consistently superior to the latest models across all datasets.