HCKGL: Hyperbolic collaborative knowledge graph learning for recommendation

Recently, the integration of knowledge graph and recommendation system has become a hot topic. Its popular solution is firstly combining the knowledge graph and user–item interaction graph to generate a unified Collaborative Knowledge Graph (CKG), and then learn the representations of users and items by applying graph convolutional networks to aggregate high-order neighbor information between entities in CKG. However, existing related methods mainly focus on learning representations in the Euclidean space, posing challenges in capturing the hierarchical structure and intricate relational logic between users and items. In view of this, we propose a novel hyperbolic CKG learning model HCKGL for recommendation, which leverages relation-specific curvature and attention-based geometric transformations to preserve the inherent features of CKG. Additionally, we address two significant challenges that existing methods have often overlooked. Firstly, in order to capture the relationship dependencies between neighbors and accurately calculate the contribution of neighbor information, we propose a hyperbolic graph attention network (HGAT), which combines the curvature of the relationship to assign weights. Secondly, we present a new graph contrastive learning technique (HMCL) that utilizes the hyperbolic embedding propagation and multi-level contrastive learning to improve the representations of users and items. Comprehensive experimental results on two widely used datasets demonstrate that HCKGL outperforms state-of-the-art baselines. The source code for our model is publicly available at: https://github.com/GDM-SCNU/HCKGL.