A Survey on Knowledge Graph-Based Recommender Systems
Chinese Academy of Sciences · Hong Kong University of Science and Technology · +6 more institutions
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 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…
Citation impact
- FWCI
- 105.82
- Percentile
- 100%
- References
- 115
Authors
7- QGQingyu GuoCorresponding
Chinese Academy of Sciences, Hong Kong University of Science and Technology, Institute of Computing Technology
- FZFuzhen Zhuang
Chinese Academy of Sciences, Institute of Computing Technology, University of Chinese Academy of Sciences
- CQChuan Qin
University of Science and Technology of China, Baidu (China)
- HZHengshu Zhu
Baidu (China)
- XXXing Xie
Microsoft Research Asia (China)
Topics & keywords
- Computer science
- Recommender system
- Knowledge graph
- Information retrieval
- Graph
- Theoretical computer science
Funding
- NNNational Natural Science Foundation of ChinaAwards: 61836013, U1836206, U1811461, 61773361, U1836206, U1811461, 71531001, 61773361
- YIYouth Innovation Promotion Association of the Chinese Academy of SciencesAward: 2017146
- NKNational Key Research and Development Program of ChinaAward: 2018YFB1004300
- YIYouth Innovation Promotion Association