A Survey on Knowledge Graph-Based Recommender Systems

Abstract

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold start. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives. On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. On the other hand, we introduce datasets used in these works. Finally, we propose several potential research directions in this field.

17 pages, 1 figure

References in corpus (10)

• KGAT: Knowledge Graph Attention Network for Recommendation
• Convolutional Neural Networks for Sentence Classification
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
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• IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
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• Which Knowledge Graph Is Best for Me?
• An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation