Exploring High-Order User Preference with Knowledge Graph for Recommendation

Caijun Xu, Fuwei Zhang, Zhao Zhang, Fuzhen Zhuang, Rui Liu

Abstract: Knowledge Graph (KG) has proven its effectiveness in recommendation systems. Recent knowledge-aware recommendation methods, which utilize graph neural networks and contrastive learning, underestimate two issues: 1) The neglect of modeling the latent relationships between users and entities; 2) The insufficiency of traditional cross-view contrastive learning whose domain is incapable of covering all nodes in a graph. To address these issues, we propose a novel model named Knowledge-aware User Preference Network (KUPN). Specifically, KUPN first constructs the relational preference view containing a new graph named User Preference Graph (UPG) to model the potential relationships between users and entities. Then, we adopt a novel attentive information aggregation to learn the UPG. In addition, we obtain semantic information of users and entities from collaborative knowledge view which consists of KG and Interaction Graph (IG) as supplementary. Finally, we apply a cross-view contrastive learning for complete domains between dynamic relational preference view and collaborative knowledge view. Extensive experiments on three real-world datasets demonstrate the superiority of KUPN against the state-of-the-art methods.