A Survey on Knowledge Graph-Based Recommender Systems

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 se...

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Vydáno v:IEEE transactions on knowledge and data engineering Ročník 34; číslo 8; s. 1
Hlavní autoři: Guo, Qingyu, Zhuang, Fuzhen, Qin, Chuan, Zhu, Hengshu, Xie, Xing, Xiong, Hui, He, Qing
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Avatars
Blood
Electronic mail
Explainable Recommendation
Feature extraction
Knowledge Graph
Knowledge representation
Machine learning
Motion pictures
Recommender System
Recommender systems
User experience
ISSN:1041-4347, 1558-2191
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Shrnutí: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 problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned 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 group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.
Bibliografie:ObjectType-Article-1
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2020.3028705