Enhanced recommendation with hypergraph mixture of experts
Zihao Zhou, Zhijun Chen, Guofang Ma, Zhenghong Lin, Yanchao Tan, Shiping Wang, Carl Yang · Expert Systems with Applications · 2025
User preference modeling based on hypergraphs has shown significant potential in recommender systems. However, existing methods model complex higher-order relations rely on existing hypergraph structures, such well-constructed hypergraphs are not readily accessible in every situation. Furthermore, since existing methods perform message-passing based on the same hypergraph convolution function, they often overlook diverse relation patterns, thus lacking precision. In this work, we propose an Enhanced Recommendation Framework with Hypergraph Mixture of Experts (HMoRec). Specifically, we first employ a sparse optimal transport clustering mechanism to generate high-quality hypergraph without requiring external knowledge. Then, we model diverse higher-order interactions and enhance representation learning based on the hypergraph mixture of experts and cross-view representation fusion. Extensive experiments on four real-world multi-domain datasets have shown that our HMoRec achieves significant performance gains.