Multi-view Recommendation
Multi-View Recommendation is a recommendation framework that integrates multiple heterogeneous data sources or perspectives—such as user-item interactions, social networks, and content features—by modeling them as distinct yet complementary views, thereby improving recommendation accuracy through synergistic learning across views.
65
Publications
3.2K
Citations
255
Authors
104
Institutions
Publications per year
2017–2026
56
65
Neural News Recommendation with Attentive Multi-View Learning
Chuhan Wu, Fangzhao Wu, Mingxiao An et al. · 2019 · 253 citations · Full text
Socially-Aware Self-Supervised Tri-Training for Recommendation
Junliang Yu, Hongzhi Yin, Min Gao et al. · 2021 · 214 citations · Full text
Multi-perspective social recommendation method with graph representation learning
Hai Liu, Chao Zheng, Duantengchuan Li et al. · Neurocomputing · 2021 · 157 citations
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104
University of Science and Technology of China
Hefei, China
5 h-index
6 publications
693 citations
National University of Singapore
Singapore, Singapore
5 h-index
14 publications
931 citations

Peking University
Beijing, China
5 h-index
11 publications
621 citations
Tsinghua University
Beijing, China
4 h-index
8 publications
986 citations
Microsoft Research Asia (China)
Beijing, China
4 h-index
7 publications
894 citations
University of Pittsburgh
Pittsburgh, United States
4 h-index
6 publications
153 citations
Nanjing University of Science and Technology
Nanjing, China
3 h-index
4 publications
203 citations

Huazhong University of Science and Technology
Wuhan, China
3 h-index
9 publications
477 citations
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4 publications · 231 citations
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3 publications · 95 citations
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Repository · 3 publications · 60 citations
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TKDE · 3 publications · 231 citations
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2 publications · 87 citations
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