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

Authors

173

  1. ZD
    Zhenhua Dong

    Huawei Technologies (China), China

    6 publications · 165 citations

    6

    h-index

  2. XH
    Xiuqiang He

    Huawei Technologies (China), China

    5 publications · 145 citations

    5

    h-index

  3. FF
    Fuli Feng

    University of Science and Technology of China, China

    4 publications · 826 citations

    4

    h-index

  4. JC
    Jiawei Chen

    Zhejiang University, China

    4 publications · 817 citations

    4

    h-index

  5. ZZ
    Ziwei Zhu

    Texas A&M University, United States

    4 publications · 355 citations

    4

    h-index

1–5 of 173

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 361 citations

    5

    h-index

  2. Proceedings of the ACM Web Conference 2022

    Conference · 3 publications · 112 citations

    3

    h-index

  3. ACM Transactions on Information Systems

    3 publications · 631 citations

    3

    h-index

  4. 2

    h-index

  5. ACM Transactions on Recommender Systems

    1 publications · 20 citations

    1

    h-index

1–5