Fair Recommendation

Fair Recommendation is a recommendation approach that, as a sibling within the class of recommendation methods, specifically ensures equitable treatment and avoidance of bias across users and items by incorporating fairness-aware constraints or objectives in the recommendation process.

82

Publications

4.1K

Citations

227

Authors

95

Institutions

Publications per year

2017–2026

79

Authors

227

  1. RB
    Robin Burke

    DePaul University, United States

    9 publications · 382 citations

    9

    h-index

  2. YZ
    Yongfeng Zhang

    Rutgers, The State University of New Jersey, United States

    8 publications · 733 citations

    8

    h-index

  3. YG
    Yingqiang Ge

    Rutgers, The State University of New Jersey, United States

    8 publications · 774 citations

    8

    h-index

  4. MM
    Masoud Mansoury

    Eindhoven University of Technology, Netherlands

    6 publications · 143 citations

    6

    h-index

  5. YL
    Yunqi Li

    Rutgers, The State University of New Jersey, United States

    6 publications · 682 citations

    6

    h-index

1–5 of 227

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 213 citations

    8

    h-index

  2. Information Processing & Management

    3 publications · 169 citations

    3

    h-index

  3. ACM Transactions on Information Systems

    4 publications · 439 citations

    3

    h-index

  4. 3

    h-index

  5. AI Magazine

    2 publications · 38 citations

    2

    h-index

1–5