Recommendation Bias
Recommendation Bias is a systematic distortion in algorithmic content curation processes whereby user exposure to political information is skewed due to the preferential amplification of specific viewpoints, engagement-driven optimization, or platform design choices inherent in recommender systems.
132
Publications
8.1K
Citations
394
Authors
191
Institutions
Publications per year
2017–2026
110
132
Causal Intervention for Leveraging Popularity Bias in Recommendation
Yang Zhang, Fuli Feng, Xiangnan He et al. · 2021 · 415 citations · Full text
Mario Haim, Andreas Graefe, Hans‐Bernd Brosius · Digital Journalism · 2017 · 390 citations
1–5 of 132
394
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191
University of Science and Technology of China
Hefei, China
10 h-index
30 publications
6.1K citations
Tsinghua University
Beijing, China
7 h-index
15 publications
1.4K citations
University of Colorado Boulder
Boulder, United States
4 h-index
7 publications
418 citations
National University of Singapore
Singapore, Singapore
4 h-index
8 publications
1.1K citations

Universitat Pompeu Fabra
Barcelona, Spain
3 h-index
7 publications
211 citations

University of Electronic Science and Technology of China
Chengdu, China
3 h-index
4 publications
132 citations
University of Technology Sydney
Sydney, Australia
3 h-index
7 publications
257 citations
Max Planck Institute for Software Systems
Saarbrücken, Germany
3 h-index
5 publications
191 citations
1–8 of 191
Repository · 13 publications · 511 citations
12
h-index
6 publications · 389 citations
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Repository · 5 publications · 204 citations
5
h-index
4 publications · 670 citations
4
h-index
3 publications · 138 citations
3
h-index
1–5