Abstract: Knowledge Graph (KG) owns rich structured information, which can effectively alleviate the sparsity and cold start problem of recommendation model and improve the accuracy and interpretability of recommendation. In recent years, the end-to-end recommendation models incorporating knowledge graph have become a technological trend. This paper proposes a KG recommendation model incorporating the influence effects of similar users. The proposed method expands the interaction between users and items to effectively utilize the knowledge graph. Firstly, a graph neural network neighbor aggregation strategy and an attention mechanism are used to capture two higher-order representations of users and items on the knowledge graph respectively. Then, an influence enhancement layer is designed to capture potential representations of similar users influence effects according to their influence effects. Finally, these three representations are fed together into a multi-layer perceptron to output prediction scores. Experimental results on real datasets show the effectiveness and efficiency of the proposed model.

Key words: knowledge graph, recommender system, attention mechanism, influence effect