DHFM: Diversity Enhanced 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 neural networks (GNNs) based models are proposed to model feature interactions by predicting the existence of edges between pairwise nodes that represent features. However, these models can only directly model two-order feature interactions. Although stacking multiple GNN layers can implicitly capture the arbitrary high-order feature interactions, it may lead to the over-smoothing problem. To this end, we propose DHFM, D iversity enhanced H ypergraph F actorization M achines that incorporate hypergraphs into feature interaction modeling, which can model the diverse feature interactions of different orders explicitly. Specifically, order-wise hyperedge predictors are proposed to discover beneficial feature interactions and explicitly model the feature interactions of different orders. In addition, diversity measures are introduced in hyperedge predictors and in the results of feature interactions to make discovered feature interactions as diverse as possible and avoid generating correlated errors. Extensive experiments on four real-world datasets demonstrate the superiority of the proposed model. In addition, the case study is conducted to further justify the effectiveness of the proposed model.