Abstract: For the problem of data sparsity in recommendation systems,traditional collaborative filtering methods fail to capture the correlations between auxiliary information,which decreases the accuracy of recommendation.In this paper,we propose the KGE-CF model by introducing the knowledge graph as auxiliary information,and utilizing the multi-source structured data in knowledge graph to effectively alleviate data sparsity.KGE-CF integrates multi-layer perceptron to capture the high-order nonli-near features to learn deeper interaction information between users and items,which can improve the quality of recommendation.Specifically,we first map the user's historical interaction items with the corresponding entities in the knowledge graph,and use the translation model of the knowledge graph for training,through that we can get the entity embedding vector and the relation embedding vector,moreover the model can learn a richer user vector by interest disseminating.Then,after concatenating the obtained user vector and the item vector,we send it into the multi-layer perceptron to capture the high-order feature information between the user and the item.Finally,a sigmoid function is used to obtain the user's preference probability for candidate items.The experimental results on the real-world datasets prove that the proposed KGE-CF model in this paper has achieved the best recommendation performance than state-of-art methods.

Key words: Collaborative filtering, Data sparsity, Knowledge graph, Multi-layer perceptron, Recommendation system