Knowledge-enhanced recommendation via dynamic co-attention and high-order connectivity

Abstract

Knowledge graph (KG) based recommender systems have shown promise in improving accuracy and interpretability. They reveal the intrinsic relationships of knowledge through the associations and paths between entities for personalized recommendations. However, existing approaches do not adequately consider the high-order connections between neighboring nodes in the relational graph, resulting in a lack of sufficient capture of structured information. In this paper, we propose a knowledge-enhanced recommendation model via dynamic co-attention and high-order connectivity (DCHC) to address this issue. First, we construct a hybrid graph by aligning users and items in the user-item bipartite graph with entities in the KG. As a result, we are able to simultaneously consider the interaction between users and items as well as the entity information in the KG, thereby gaining a more comprehensive understanding of user behavior and interests. Second, we explicitly model the high-order connections between entities through the hybrid structured graphs in an end-to-end manner. Therefore, we not only explored the complex interactive relationships between entities but also ensured the accurate capture of structural information in the graph. Third, we employ a dynamic co-attention mechanism to enhance the representation of users and items, effectively exploiting the potential correlation between them. We therefore effectively exploited the potential correlation between users and items and successfully integrating these relationships into their representations. Extensive experiments conducted on three benchmarks demonstrate that DCHC outperforms state-of-the-art KG-based recommendation methods.