Review-enhanced contrastive learning on knowledge graphs for recommendation
Yun Liu, Natthawut Kertkeidkachorn, Jun Miyazaki, Ryutaro Ichise · Expert Systems with Applications · 2025
Knowledge graphs (KGs) have been shown to be effective in improving recommendation quality by introducing rich item properties as auxiliary information. The success of current KG-based recommender systems (RSs) lies in the capability of modeling high-quality item representations. This is achieved by identifying significant properties for items and exploring the intrinsic correlation between items on the KG. However, since current KG-based works only focus on learning user implicit knowledge from KGs through items, the limited user-item interaction behavior is still an obstacle to learning high-quality user representations. Furthermore, irrelevant connections in the KG may lead to erroneous messaging during the process of high-order graph feature learning of users and items. This could subsequently result in the inaccurate recommendation of items to users. To overcome above limitations, we propose a Review-enhanced Contrastive Learning on KGs (RCLKG) model for high-quality recommendation. We first construct a review-enhanced KG by exploring user explicit preferences in reviews with the extracted review entities. Then, we design a review-aware self-augmentation mechanism that seamlessly integrates explicit review knowledge with item-aligned KGs to discard irrelevant neighbor nodes of users and items. Furthermore, we develop a global-level graph aggregation schema with a refined constraint on the merged denoising KG to further optimize the denoising KG generation by considering high-order connections with less erroneous messaging. Finally, experimental results on the rating prediction and the click-through rate prediction (CTR) tasks with three real-word datasets demonstrate the superiority of our proposed RCLKG model in comparison with the state-of-the-art baselines. • A novel review-enhanced contrastive learning model on KGs called RCLKG is proposed. • Review knowledge is injected into the knowledge graph as explicit knowledge of users. • Review-enhanced self-augmentation mechanism filters out irrelevant nodes in the KG. • Global-level graph encoder on merged denoising KG refines denoising KG generation. • Extensive experiments demonstrate the superiority of RCLKG.