Hierarchy Knowledge-aware Contrastive Learning for Recommendation

Knowledge graphs (KGs) have demonstrated exceptional effectiveness within the domain of recommendation systems.However, in recommendation systems, the high-quality representation of KG is hindered by noise and data sparsity.Additionally, user-item(UI) interactions dominate item node representations, with minimal influence from the knowledge graph.Furthermore, single-level contrastive learning (CL) inadequately captures implicit information in node embeddings.In order to address those issue, we propose the Hierarchy Knowledgeaware Contrastive Learning (HKCL) framework, which utilizes view enhancement in CL.Firstly, we employ an edge learner to reduce noise interference during the learning process by refining the representation of the graph.Secondly we encode the UI interaction graph and KG using Graph Neural Network (GNN).Finally, during the CL process, we use a finer-grained hierarchical CL method to enhance node representations, thereby discovering more potential features to alleviate the issue of data sparsity.Specifically, we conduct CL at the user-item level, item level, and entity-item level, making the CL more compatible with recommendation learning.Empirical findings from three extensive real-world datasets illustrate that our approach improves the accuracy of recommendation result.