SR-HyperFM: Sample Relationship Aware Hypergraph Factorization Machines for Feature Interaction Modeling

Feature interaction modeling, which exploits interactive information between features, has been widely explored in various applications. Recently, many graph or hypergraph structures-based models have been proposed to model feature interactions by predicting the existence of edges or hyperedges among nodes. However, these models lack the capability to capture the inherent comparability among samples, where multiple samples exhibit both shared and distinct characteristics, and such comparable relationships are often beneficial for prediction. To this end, we propose SR-HyperFM, Sample Relationship aware Hypergraph Factorization Machines, which incorporate sample comparable relationships into feature interaction modeling, leveraging both shared features and critical differences among samples. Specifically, the sample relationship aware hypergraph construction module is introduced to fully capture the comparable relationships among samples and discover beneficial high-order feature interactions. In addition, the dual hypergraph message passing module explicitly models feature interactions by exploiting these inherent relationships. Extensive experiments on four real-world datasets demonstrate the superiority of SR-HyperFM. In addition, case studies are conducted to further justify the effectiveness of SR-HyperFM.