Review-enhanced contrastive learning on knowledge graphs for recommendation

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.