Most current graph neural networks (GNNs) are designed from the view of methodology and rarely consider the inherent characters of graph. Although the inherent characters may impact the performance of
we propose an improved knowledge representation learning model Cluster TransD and a recommendation model Cluster TransD-GAT based on knowledge graph and graph attention networks, where the Cluster Tra