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

Authors

582

  1. ZD
    Zhenhua Dong

    Huawei Technologies (China), China

    11 publications · 454 citations

    11

    h-index

  2. XH
    Xiuqiang He

    Huawei Technologies (China), China

    8 publications · 365 citations

    8

    h-index

  3. XH
    Xiangnan He

    University of Science and Technology of China, China

    8 publications · 669 citations

    8

    h-index

  4. JC
    Jiawei Chen

    Zhejiang University, China

    7 publications · 260 citations

    7

    h-index

  5. WP
    Weike Pan

    Shenzhen University, China

    6 publications · 323 citations

    6

    h-index

1–5 of 582

Venues

  1. arXiv (Cornell University)

    Repository · 17 publications · 448 citations

    13

    h-index

  2. 5

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 5 publications · 215 citations

    5

    h-index

  4. ACM Transactions on Information Systems

    4 publications · 51 citations

    4

    h-index

  5. SSRN Electronic Journal

    Repository · 4 publications · 134 citations

    4

    h-index

1–5