Bias Evaluation

Bias Evaluation is a systematic assessment framework within the domain of recommender systems research that identifies, quantifies, and analyzes distortions or systematic deviations in recommendation outcomes arising from algorithmic, data, or user-related factors, thereby distinguishing it from general performance evaluation by focusing specifically on inequities and skewed representations.

234

Publications

20.6K

Citations

759

Authors

342

Institutions

Publications per year

2017–2026

121

Authors

759

  1. XH
    Xiangnan He

    University of Science and Technology of China, China

    6 publications · 942 citations

    6

    h-index

  2. JC
    Jiawei Chen

    Zhejiang University, China

    5 publications · 914 citations

    5

    h-index

  3. FF
    Fuli Feng

    University of Science and Technology of China, China

    4 publications · 826 citations

    4

    h-index

  4. PC
    Pablo Castells

    Universidad Autónoma de Madrid, Spain

    4 publications · 370 citations

    4

    h-index

  5. RC
    Rocío Cañamares

    Universidad Autónoma de Madrid, Spain

    3 publications · 204 citations

    3

    h-index

1–5 of 759

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 535 citations

    11

    h-index

  2. Information Processing & Management

    5 publications · 365 citations

    5

    h-index

  3. Psychiatric Annals

    4 publications · 97 citations

    4

    h-index

  4. Academy of Management Review

    AMR · 4 publications · 609 citations

    4

    h-index

  5. 4

    h-index

1–5