Knowledge Graph Recommendation
Knowledge Graph Recommendation is a recommendation method that leverages structured knowledge representations, in which entities and their semantic relationships are modeled as a graph, to enhance the accuracy and explainability of learning resource suggestions by exploiting relational reasoning over interconnected educational data.
197
Publications
13.9K
Citations
759
Authors
249
Institutions
Publications per year
2017–2026
190
197
Xiang Wang, Xiangnan He, Yixin Cao et al. · 2019 · 2.1K citations · Full text
A Survey on Knowledge Graph-Based Recommender Systems
Qingyu Guo, Fuzhen Zhuang, Chuan Qin et al. · IEEE Transactions on Knowledge and Data Engineering · 2020 · 845 citations · Full text
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Rutgers, The State University of New Jersey, United States
5 publications · 384 citations
5
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1–5 of 759
249
University of Science and Technology of China
Hefei, China
11 h-index
25 publications
4.3K citations

Chinese Academy of Sciences
Beijing, China
10 h-index
19 publications
3.4K citations
Tsinghua University
Beijing, China
9 h-index
27 publications
2.4K citations
University of Chinese Academy of Sciences
Beijing, China
9 h-index
21 publications
2.8K citations
Shandong University
Jinan, China
8 h-index
22 publications
2.8K citations
Microsoft Research Asia (China)
Beijing, China
8 h-index
19 publications
2.1K citations

Renmin University of China
Beijing, China
7 h-index
19 publications
1.9K citations

University of Electronic Science and Technology of China
Chengdu, China
6 h-index
21 publications
1.5K citations
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10 publications · 322 citations
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10 publications · 448 citations
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8 publications · 484 citations
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TKDE · 9 publications · 1.2K citations
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Repository · 7 publications · 174 citations
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