Dual-view Enhanced Knowledge Contrastive Learning for Recommendation

To alleviate the problem of data sparsity in traditional recommender systems, knowledge graphs (KGs) serve as side information to provide rich semantic information. However, existing KG-based recommendations have the following two issues. Knowledge noise: KG contains many recommendation-irrelevant relations, which would be amplified during representation learning. Knowledge sparsity: the number of triplets corresponding to some entities is extremely sparse, leading to detrimental learning of semantic information. Inspired by this, we propose a novel Dual-view Enhanced Knowledge Contrastive Learning (DKCL) to address the two problems. Specifically, we first devise a relation-aware attention mechanism to learn the relation scores and discard a certain percentage of relations with lower scores, which effectively suppresses the knowledge noise introduced by task-irrelevant relations. Secondly, we utilize clustering algorithm to generate semantic entities and replace the original entities with them. In this way, each semantic entity corresponds to a larger number of triplets than the original entities. Finally, we develop a cross-view contrastive learning pattern to bridge the knowledge semantic signals with the collaborative signals, which enhances the item representations in the user-item graph with the denoised semantic information. Extensive experiments on three real-world datasets demonstrate the superiority of our proposed model compared to state-of-the-art methods.