Knowledge Graph-based Recommendation
Knowledge Graph-Based Recommendation is a recommendation approach that leverages structured knowledge from a knowledge graph as its primary source of side information to enhance recommendation accuracy, particularly in cold-start scenarios, by exploiting semantic relationships between entities.
457
Publications
27.7K
Citations
1.7K
Authors
520
Institutions
Publications per year
2017–2026
439
Variants
Knowledge Graph-enhanced Recommendation
457
Xiang Wang, Xiangnan He, Yixin Cao et al. · 2019 · 2.1K citations · Full text
Hongwei Wang, Fuzheng Zhang, Xing Xie et al. · 2018 · 1K citations · Full text
Knowledge Graph Convolutional Networks for Recommender Systems
2019 · 967 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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Microsoft Research Asia (China)
Beijing, China
15 h-index
16 publications
5.3K citations
University of Science and Technology of China
Hefei, China
15 h-index
15 publications
4.2K citations
Tsinghua University
Beijing, China
15 h-index
15 publications
1K citations

Chinese Academy of Sciences
Beijing, China
13 h-index
16 publications
1.4K citations
Wuhan University
Wuhan, China
12 h-index
12 publications
900 citations
Shandong University
Jinan, China
11 h-index
11 publications
2.5K citations

Shanghai Jiao Tong University
Shanghai, China
10 h-index
10 publications
4.1K citations

Zhejiang University
Hangzhou, China
10 h-index
10 publications
857 citations
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29 publications · 1K citations
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23 publications · 1K citations
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TKDE · 15 publications · 1.5K citations
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Repository · 16 publications · 623 citations
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11 publications · 465 citations
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