Integrating Social and Knowledge Graphs in GNN-Based Recommender Systems

Graph Neural Networks (GNN) have emerged as a powerful tool in recommendation systems due to their ability to adeptly model complex relational data. Despite their potential, existing GNN-based approaches often fail to fully harness the synergistic benefits of integrating social networks and knowledge graphs into the recommendation process, overlooking the nuanced differences between these data sources. To address these gaps, we propose a novel Integrating Social and Knowledge Graphs (ISKG) framework tailored for GNN-based Recommender Systems. The ISKG model amalgamates user-item interactions, social connections, and knowledge graph insights into a unified representation, enhancing the recommendation quality through a multi-faceted approach. It starts with generating initial embeddings, progresses through a fusion layer for feature amalgamation, and refines these features in successive propagation layers. An innovative Adaptive Weighting Mechanism dynamically balances the influence of social and knowledge graph-enhanced features, leading to a Prediction Layer that finalizes the recommendations. Our comprehensive evaluation showcases ISKG's superiority over conventional baselines, highlighting its ability to achieve an effective balance between social and knowledge-based recommendations, thus paving the way for more accurate and nuanced recommendation systems. The project details are available at https://yuzengyi.github.io/ISKG/.