Pairwise Ranking

Pairwise Ranking is a relevance ranking method that determines the relative order of items by comparing pairs of user-item interactions to optimize the likelihood that a more relevant item is ranked higher than a less relevant one.

233

Publications

27.8K

Citations

683

Authors

311

Institutions

Publications per year

2017–2026

95

Authors

683

  1. SO
    Sewoong Oh

    University of Illinois Urbana-Champaign, United States

    5 publications · 435 citations

    5

    h-index

  2. ŁC
    Łászló Csató

    Corvinus University of Budapest, Hungary

    4 publications · 217 citations

    4

    h-index

  3. MJ
    Martin J. Wainwright

    University of California, Berkeley, United States

    4 publications · 194 citations

    4

    h-index

  4. NB
    Nihar B. Shah

    University of California, Berkeley, United States

    4 publications · 194 citations

    4

    h-index

  5. BD
    Bernard De Baets

    Ghent University, Belgium

    3 publications · 68 citations

    3

    h-index

1–5 of 683

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 685 citations

    13

    h-index

  2. Biometrika

    5 publications · 160 citations

    5

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 172 citations

    4

    h-index

  4. European Journal of Operational Research

    4 publications · 346 citations

    4

    h-index

  5. The Annals of Mathematical Statistics

    4 publications · 214 citations

    4

    h-index

1–5