Graph Enhanced Neural Interaction Model for recommendation

Abstract

Since user-item interactions in recommender systems can be naturally modeled as a bipartite graph, recent studies have started to incorporate graph neural networks (GNNs) to learn user and item representations. However, existing GNN-based models for recommendation usually emphasize the graph structure but neglect the rich node (i.e., users and items) features or linearly integrate the node features without considering the interactions among these features, which leads to a suboptimal recommendation model. Considering the features (e.g., categorical features) are often discrete, sparse, and high-dimensional, how to jointly utilize the graph structure and node features while considering the feature interactions is a major challenge. In this paper, we propose Graph Enhanced Neural Interaction Model (GENIM), a novel graph recommendation model consisting of three parts: (1) graph convolution layers that recursively propagate the encoded node features on the user-item bipartite graph; (2) the neural feature interaction layer that learns node feature interactions, which contains rich signals for predictive analytics; (3) an optional hashing-based embedding layer that is used to reduce the model size. Extensive experiments conducted on two real-world datasets show that our model outperforms other state-of-the-art solutions.