Bias-aware Recommendation
Bias-Aware Recommendation is a recommendation approach that explicitly identifies, models, and mitigates systematic distortions arising from data, algorithmic, or user behavioral biases to ensure fair and accurate personalization.
180
Publications
10.7K
Citations
582
Authors
239
Institutions
Publications per year
2017–2026
147
Parents
180
Causal Intervention for Leveraging Popularity Bias in Recommendation
Yang Zhang, Fuli Feng, Xiangnan He et al. · 2021 · 415 citations · Full text
Item popularity and recommendation accuracy
Harald Steck · 2011 · 330 citations
1–5 of 180
582
11
h-index
8
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8
h-index
7
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6
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239
University of Science and Technology of China
Hefei, China
13 h-index
40 publications
4.5K citations
Huawei Technologies (China)
Shenzhen, China
10 h-index
34 publications
1.4K citations
Tsinghua University
Beijing, China
10 h-index
30 publications
1.6K citations
Tencent (China)
Shenzhen, China
8 h-index
17 publications
1.4K citations

Renmin University of China
Beijing, China
7 h-index
38 publications
3.2K citations

Shenzhen University
Shenzhen, China
6 h-index
22 publications
1K citations
Rutgers, The State University of New Jersey
New Brunswick, United States
5 h-index
24 publications
3K citations

Cornell University
Ithaca, United States
5 h-index
12 publications
836 citations
1–8 of 239
Repository · 17 publications · 448 citations
13
h-index
Conference · 5 publications · 740 citations
5
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TKDE · 5 publications · 215 citations
5
h-index
4 publications · 51 citations
4
h-index
Repository · 4 publications · 134 citations
4
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