SKRAG: Sampling on Knowledge Graph for Recommendation with Relational Path-aware Graph Neural Network

Recently, the recommender system combining knowledge graph (KG) is gaining traction from researchers. However, current research faces two problems: The first is most existing works ignore the fact that real-world knowledge graphs are often noisy and contain lots of recommendation-irrelevant connections. The second is item-linked relations can reflect different attributes of items, however, previous works usually ignore the different relational semantics carried by different types of relational paths, leading to suboptimal recommendation performance. To address these issues, we propose a novel model: Sampling on Knowledge Graph for Recommendation with Relational Path-aware Graph Neural Network (SKRAG). We design a relational path-aware sampling method to extract recommendation-relevant information in KG, further mitigating the impact of KG noise; Then, we utilize relational path information to capture item long-range knowledge associations with explicit attribute semantics and extract user potential purposes for learning high-quality item and user representations. We conduct extensive experiments on three datasets, and the experimental results show the superiority of our model.