Provider Fairness

Provider Fairness is a normative principle in algorithmic fairness that pertains to the equitable treatment of content providers or producers in recommendation systems, distinguished by its focus on ensuring that exposure, visibility, or downstream opportunities are distributed among providers in a manner that respects fairness criteria such as proportionality, non-discrimination, or meritocratic allocation.

27

Publications

975

Citations

71

Authors

45

Institutions

Publications per year

2017–2026

20

Authors

71

  1. MS
    María Salamó

    Universitat de Barcelona, Spain

    2 publications · 80 citations

    2

    h-index

  2. BT
    Bruno Tuffin

    Institut national de recherche en sciences et technologies du numérique, France

    2 publications · 48 citations

    2

    h-index

  3. LH
    Linhai He

    University of California, Berkeley, United States

    2 publications · 197 citations

    2

    h-index

  4. YW
    Yao Wu

    Shanghai Jiao Tong University, China

    2 publications · 91 citations

    2

    h-index

  5. GS
    Gwendal Simon

    Institut de Recherche en Informatique et Systèmes Aléatoires, France

    2 publications · 48 citations

    2

    h-index

1–5 of 71

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 86 citations

    4

    h-index

  2. SSRN Electronic Journal

    Repository · 2 publications · 29 citations

    2

    h-index

  3. 2

    h-index

  4. 2

    h-index

  5. ACM Transactions on Recommender Systems

    1 publications · 39 citations

    1

    h-index

1–5