Category-aware Multi-relation Heterogeneous Graph Neural Networks for session-based recommendation

Session-based recommendation (SBR) is one of the hot research areas in recent years. Various SBR models have been proposed, of which graph neural network (GNN)-based models are shown to have the state-of-the-art performance. Items’ category information is an important piece of information and should be utilized in SBR models to improve model performance. In this paper, we introduce a principle way to incorporate items’ category information for SBR. More specifically, we propose a new SBR model, Category-aware Multi-relation Heterogeneous Graph Neural Networks (CM-HGNN). In CM-HGNN, we first propose to construct an item–category heterogeneous graph (ICHG) to model both category–category relation and item–category relation. More specifically, we propose to transform the sequential information contained in a session into a heterogeneous graph with both item nodes and category nodes, by which items and categories can learn from each other and the items belonging to the same category can also perceive one another. As a result, multiple interests in a session could be more effectively captured. Then, a multi-relation heterogeneous graph convolution method is proposed to extract the multiple relation information contained in the ICHG. Extensive experiments are conducted on three widely used real-world datasets, and the results suggest that the proposed CM-HGNN outperforms the state-of-the-art SBR models.