User Fairness
User Fairness is a fairness criterion in recommendation systems that ensures equitable treatment or outcomes for individual users by mitigating biases in personalization processes that may systematically disadvantage certain users based on sensitive attributes or positional advantages.
370
Publications
25.8K
Citations
1.1K
Authors
389
Institutions
Publications per year
2017–2026
246
370
Fairness of Exposure in Rankings
Ashudeep Singh, Thorsten Joachims · 2018 · 569 citations · Full text
1–5 of 370
1.1K
13
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7
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Rutgers, The State University of New Jersey, United States
6 publications · 501 citations
6
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6
h-index
1–5 of 1.1K
389
Tsinghua University
Beijing, China
13 h-index
38 publications
3.5K citations

Xidian University
Xi'an, China
12 h-index
37 publications
3.1K citations

Beijing University of Posts and Telecommunications
Beijing, China
12 h-index
46 publications
1K citations
Lancaster University
Lancaster, United Kingdom
11 h-index
20 publications
2.8K citations

Southeast University
Nanjing, China
8 h-index
20 publications
957 citations

Beihang University
Beijing, China
8 h-index
17 publications
622 citations
Queen Mary University of London
London, United Kingdom
8 h-index
22 publications
998 citations
Princeton University
Princeton, United States
8 h-index
9 publications
1.5K citations
1–8 of 389
24 publications · 2.8K citations
21
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17 publications · 1.1K citations
15
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Repository · 16 publications · 614 citations
14
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13 publications · 1.1K citations
13
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16 publications · 455 citations
13
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1–5