Improving hypergraph convolution network collaborative filtering with feature crossing and contrastive learning

Organizing user-item interaction data into a graph has brought many benefits to recommendation methods. Compared with the user-item bipartite graph structure, a hypergraph structure provides a natural way to directly model high-order correlations among users or items. Hypergraph Convolution Network (HGCN) has the capability of aggregating and propagating latent features of nodes in the hypergraph nonlinearly. Recently, recommendation models based on simplified HGCN have shown good performance. However, such models lose the powerful expression ability of feature crossing and suffer from limited labeled data. To tackle these two problems, a framework called HGCN-CC is proposed to improve HGCN with feature Crossing and Contrastive learning. Specifically, HGCN is combined with a feature cross network in a parallel manner to balance between feature crossing and over smoothing. By such a design, HGCN-CC not only utilizes simplified propagation operation in HGCN to capture high-order correlations among users or items, but also enjoys the powerful expressing ability of high-order feature interactions. Furthermore, HGCN-CC resorts to contrastive learning to help learn good representations. Under the HGCN-CC framework, two models called item-based HGCN-CC (I-HGCN-CC) and user-based HGCN-CC (U-HGCN-CC) are constructed to emphasize different aspects of data. Results of extensive experiments on four benchmark datasets demonstrate that proposed models have superiority in modelling hypergraph structure data for recommendations.