Two-sided Recommendation
Two-Sided Recommendation is a multi-stakeholder recommendation framework that specifically mediates preferences and interactions between two distinct user groups to optimize mutual satisfaction.
21
Publications
1.3K
Citations
66
Authors
37
Institutions
Publications per year
2017–2026
18
21
Rishabh Mehrotra, James O. McInerney, Hugues Bouchard et al. · 2018 · 278 citations
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
2020 · 182 citations · Full text
Computing, Economics, Auction Theory +15
Yao Wu, Jian Cao, Guandong Xu et al. · 2021 · 81 citations · Full text
Computing, Data Science, Customer Relationship Management +15
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37

Shanghai Jiao Tong University
Shanghai, China
2 h-index
4 publications
182 citations
Ctrip
Xuhui, China
2 h-index
2 publications
91 citations
Indian Institute of Technology Kharagpur
Kharagpur, India
2 h-index
4 publications
434 citations

The University of Sydney
Sydney, Australia
2 h-index
12 publications
732 citations
Max Planck Institute for Software Systems
Saarbrücken, Germany
2 h-index
3 publications
399 citations
University of Technology Sydney
Sydney, Australia
2 h-index
2 publications
91 citations

Anhui University
Hefei, China
1 h-index
4 publications
120 citations
University of Science and Technology of China
Hefei, China
1 h-index
1 publications
20 citations
1–8 of 37
Repository · 2 publications · 35 citations
2
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1 publications · 30 citations
1
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1 publications · 65 citations
1
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JBR · 1 publications · 29 citations
1
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1 publications · 188 citations
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1–5