Knowledge Graph Fine-grained Modeling Network with Contrastive Learning for Recommendation

Knowledge graph (KG) is often introduced into recommendation systems because of its large amount of edge information. The method based on graph neural networks (GNNs) has gradually become the mainstream of KG-aware recommendation. However, traditional KG-aware recommendation models based on GNNs fail to utilize the dependencies of items and item attributes to model user preferences at a fine-grained level, which will result in a lack of interpretability in the model’s recommendations to users. In addition, traditional KG-aware recommendation models based on GNNs fail to mine supervision signals from the perspective of user preferences and item attributes, which will result in a lack of effective supervision signals in the model. In this study, we utilize a combination of items and attributes behind the items to model user preferences at a fine-grained level, so as to achieve independence between different user preferences. Furthermore, we utilize the KG and the user–item interaction graph (UIIG) to construct the user-specific preference similarity view and the item-specific attribute correlation views, respectively, and then apply the contrastive learning framework to effectively mine the association signals between users and between items. Based on this, we propose a novel model named Knowledge Graph Fine-grained Modeling Network with Contrastive Learning (KGFM-CL). Extensive experiments conducted on two real-world datasets demonstrate that KGFM-CL significantly outperforms state-of-the-art baseline models.