Hierarchically Coupled View-Crossing Contrastive Learning for Knowledge Enhanced Recommendation

Abstract

Knowledge enhanced recommendation algorithms focus on how to leverage auxiliary information from knowledge graphs to enhance recommendation performance. However, existing methods for knowledge enhanced recommendation often overlook the issues of the non-uniform distribution of task-relevant information: (1) Item nodes often have neighbors unevenly distributed across interaction graphs and knowledge graphs. This uneven distribution of neighbor nodes might lead to the information from certain sources being ignored during message passing, thereby reducing the quality of the learned node embeddings. (2) The implicit inclusion of noise within graph data exacerbates the aforementioned issues, further hindering the effective utilization of knowledge graph information. In this paper, we introduce a novel algorithm called hierarchically coupled view-crossing contrastive learning for knowledge enhanced recommendation to address the challenges mentioned above. Specifically, we controllably couple knowledge graph information into each layer of message passing, and then use a weighted sum of the embeddings learned hierarchically as the final node representation. In addition, we devised a view-crossing contrastive learning approach to construct two additional contrastive learning loss functions for joint training with the main task and more effectively mitigate the adverse impact of noise than the traditional contrastive learning paradigms. Extensive experiments on three real-world graph datasets show that our proposed model performs significantly better than the state-of-the-art baselines and the results of experiments involving adversarial samples indicate the robustness of our model.

Keywords

• Contrastive learning
• graph neural network
• knowledge graph
• recommender systems