Iterative Heterogeneous Graph Learning For Knowledge Graph-based Recommendation

This paper proposes a new recommendation method based on iterative heterogeneous graph learning on knowledge graphs (HGKR). HGKR incorporates graph neural networks into the message passing and aggregating of entities within a knowledge graph both at the graph and the semantic level. Furthermore, a knowledge-perceiving item filter based on an attention mechanism is designed to capture the user’s potential interest in their historical preferences for the enhancement of recommendation. Extensive experiments conducted on two datasets in the context of two recommendations reveal the excellence of the proposed method, outperforming other benchmark models.