A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network

Knowledge Graphs (KGs) are widely used in many fields of application, and especially play an essential role in recommendation systems. KGs often need to be complete, missing relationships between users and items, data sparsity, weak associations, and difficulties in knowledge inference, resulting in low credibility of recommendation results. Therefore, we propose a novel Knowledge Graph (KG) recommendation algorithms. Due to the availability of interaction data across numerous events, KGs also exhibit dynamics over time. By taking into account the temporal variable, it is possible to organise well-structured external information to connect users and items, thereby expanding user preferences to a certain extent. The proposed algorithm employs GCNs to encode the heterogeneous graph, which includes user-item interactions and the KG. It addresses the challenge of high-dimensional data by using the inner product of users and items. The algorithm uncovers potential alignment relationships and learns the embedding of user-item and relationships by applying convolutional processing to the graph data's features and performing data fusion, the new algorithm uncovers potential alignment relationships, and learns embedding of user-item and relationships. The experimental results on the Mean Reciprocal Rank (MRR) and Hits@k demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in terms of the credibility and accuracy of recommendation results.