Heterogeneous Information Diffusion Model for Social Recommendation

Social recommendation provides a promising way to alleviate data sparsity and boost recommendation performance in collaborative filtering. How to model social influence is a central problem in the social recommendation. The social data can be represented by two separate graphs together, i.e., a social (user-user) graph and an interaction (user-item) graph. To some extent, this representation leads to loss of heterogeneity. To address this problem, we propose a novel social recommendation model called Heterogeneous Information Diffusion Model (HIDM for short). Specifically, since users are involved in both two subspaces: social subspace and interaction subspace, we design a neural architecture with two modules to aggregate information of the two subspaces. The first module is social aggregation which models the social influence from social connections in layer-wise via GNNs. The second module is item aggregation which captures the item influence from interacted items in a single layer. Moreover, different message functions are designed to encode information in different subspaces. Finally, the user preference representation is obtained by fusing the influence of the two subspaces. The extensive experiments conducted on three real-world datasets show that HIDM outperforms several different state-of-the-art social recommendation models.