Knowledge-based recommendation with contrastive learning

Abstract

Knowledge Graphs (KGs) have been incorporated as external information into recommendation systems to ensure the high-confidence system. Recently, Contrastive Learning (CL) framework has been widely used in knowledge-based recommendation, owing to the ability to mitigate data sparsity and it considers the expandable computing of the system. However, existing CL-based methods still have the following shortcomings in dealing with the introduced knowledge: (1) For the knowledge view generation, they only perform simple data augmentation operations on KGs, resulting in the introduction of noise and irrelevant information, and the loss of essential information. (2) For the knowledge view encoder, they simply add the edge information into some GNN models, without considering the relations between edges and entities. Therefore, this paper proposes a Knowledge-based Recommendation with Contrastive Learning (KRCL) framework, which generates dual views from user–item interaction graph and KG. Specifically, through data enhancement technology, KRCL introduces historical interaction information, background knowledge and item–item semantic information. Then, a novel relation-aware GNN model is proposed to encode the knowledge view. Finally, through the designed contrastive loss, the representations of the same item in different views are closer to each other. Compared with various recommendation methods on benchmark datasets, KRCL has shown significant improvement in different scenarios.