Debiased Recommendation
Debiased Recommendation is a recommendation method that corrects for systematic biases in implicit feedback data to more accurately estimate user preferences by distinguishing true user interest from confounding factors such as popularity, position, or exposure.
42
Publications
2.7K
Citations
173
Authors
54
Institutions
Publications per year
2017–2026
41
Parents
42
Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang et al. · arXiv (Cornell University) · 2020 · 160 citations · Full text
AutoDebias: Learning to Debias for Recommendation
2021 · 160 citations · Full text
Computing, Data Science, Data Mining +14
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh et al. · arXiv (Cornell University) · 2016 · 143 citations · Full text
Popularity-Opportunity Bias in Collaborative Filtering
1–5 of 42
173
6
h-index
5
h-index
4
h-index
4
h-index
4
h-index
1–5 of 173
54
Tencent (China)
Shenzhen, China
7 h-index
13 publications
436 citations
Huawei Technologies (China)
Shenzhen, China
6 h-index
20 publications
588 citations
University of Science and Technology of China
Hefei, China
5 h-index
20 publications
4K citations

Renmin University of China
Beijing, China
4 h-index
13 publications
421 citations
Texas A&M University
College Station, United States
4 h-index
16 publications
1.6K citations

Shenzhen University
Shenzhen, China
3 h-index
10 publications
336 citations
Tsinghua University
Beijing, China
3 h-index
10 publications
362 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
2 h-index
3 publications
62 citations
1–8 of 54
Repository · 5 publications · 361 citations
5
h-index
Conference · 3 publications · 112 citations
3
h-index
3 publications · 631 citations
3
h-index
Conference · 2 publications · 50 citations
2
h-index
1 publications · 20 citations
1
h-index
1–5