Improving Social Recommendations with Item Relationships

Social recommendations have witnessed rapid developments for improving the performance of recommender systems, due to the growing influence of social networks. However, existing social recommendations often ignore to facilitate the substitutable and complementary items to understand items and enhance the recommender systems. We propose a novel graph neural network framework to model the multi-graph data (user-item graph, user-user graph, item-item graph) in social recommendations. In particular, we introduce a viewpoint mechanism to model the relationship between users and items. We conduct an extensive experiment on two public benchmarks, demonstrating significant improvement over several state-of-the-art models.