Related papers: A Survey on Knowledge Graph-Based Recommender Systems

A Survey on Knowledge Graph-Based Recommender Systems

URL: http://arxiv.org/abs/2003.00911v1
Date: Fri, 28 Feb 2020 02:26:30 GMT
Title: A Survey on Knowledge Graph-Based Recommender Systems
Authors: Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong and Qing He
Abstract summary: We conduct a systematical survey of knowledge graph-based recommender systems. We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation. We introduce datasets used in these works.
Score: 65.50486149662564
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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