StarRec: A Hypergraph-based Framework with Star-Expansion for Multi-Behavior Recommendation
Wenhan Zhang, Zijian Song, Yuefang Wu, Lifang Deng, Jiandong Zhang, Kaigui Bian, Bin Cui · Society for Industrial and Applied Mathematics eBooks · 2025
In modern recommendation systems, leveraging multiple types of user-item interaction behaviors (e.g., click, add-to-cart, and purchase) presents both advantages and challenges. Recent studies organize multi-behavior data into heterogeneous bipartite graphs and used graph neural networks to learn latent representations. However, these methods struggle to model higher-order interactions and capture complex dependencies across various behaviors. In this paper, we propose a novel graph construction method that converts multi-behavior interactions into dual star-expansion hypergraphs by introducing a new type of node called hypernode. Subsequently, we develop StarRec, a hypergraph-based framework for multi-behavior recommendation. StarRec utilizes a spatial-based two-stage intra-behavior message passing strategy and a cross-behavior propagation layer to accurately and efficiently propagate information, modeling both high-order and cross-behavior relationships through the hypergraphs. This approach yields comprehensive representations that enhance recommendation performance. Experimental results on two real-world datasets demonstrate the superiority of StarRec. Our extensive experiments show that StarRec significantly outperforms state-of-the-art methods while maintaining competitive model scalability.