Running summarizing tools on a new article

Abstract: In the digital age, the overwhelming amount of information necessitates advanced recommendation systems to deliver personalized content. However, these systems face significant challenges, such as sparse user-item interactions and long-tail bias. Recent studies construct structural learning or self-supervised learning on the interaction graph achieving a positive impact on alleviating the problems, but the interaction data itself may be far too little to solve the problems. While knowledge graphs (KGs) offer a promising solution by providing semantic depth to recommendations, their integration often introduces noise from redundant knowledge. Addressing these critical gaps, this study proposes a knowledge-guided interest contrast (KGIC) to enhance recommendations, which innovatively harmonizes collaborative filtering with semantic insights from KG. The KGIC model introduces three key innovations: (1) a knowledge filtering mechanism that selectively leverages interest-relevant signals from the knowledge graph to encode interest and avoid redundant knowledge interference; (2) an adaptive graph augmentation strategy that enhances the interaction graph based on semantic-aware interest propagation and interaction intensity estimation; and (3) a self-supervised contrastive learning task that mitigates long-tail bias and sparsity issues by homogenizing the embedding distribution between augmented views. The extensive evaluation reveals the superiority of KGIC with knowledge filtering and graph augmentation for recommendation.