Knowledge Graph Bidirectional Interaction Graph Convolutional Network for Recommendation

Recently, the recommended method based on the Knowledge Graph (KG) has become a hot research topic in modern recommendation systems. Most researchers use assistive information such as entity attributes in KG to improve recommendation performance and alleviate Collaborative Filtering (CF) sparsity and cold start problems. The most recent technical trend is to develop end-to-end models based on the Graph Convolutional Network (GCN). In this paper, we propose a Knowledge Graph Bidirectional Interaction Graph Convolution Network for recommendation (KBGCN). This method is used to refine the embedded representation of node by recursively delivering messages from the neighbors (attributes or items) of the node (entity) and applies the knowledge aware attention mechanism to distinguish the contributions of different neighbors based of the same node. It uses neighbors of each entity in KG as the view of this entity, which can be extended by expanding the view of Multi-hop neighbors to mine high-order connectivity information existing in KG automatically. We apply the proposed method to three real-world datasets. KBGCN is better than seven KG-based baselines in recommendation accuracy and the two state-of-the-art GCN-based recommendations frameworks.