Pairwise Ranking Models

Pairwise Ranking Models is a retrieval framework that ranks documents by learning to predict relative preferences between pairs of documents with respect to a given query, distinguishing it from pointwise or listwise approaches by focusing on pairwise comparisons to optimize ranking performance.

227

Publications

16.6K

Citations

651

Authors

265

Institutions

Publications per year

2017–2026

100

Variants

Pairwise Ranking Frameworks

Authors

651

  1. MJ
    Martin J. Wainwright

    University of California, Berkeley, United States

    4 publications · 87 citations

    4

    h-index

  2. SO
    Sewoong Oh

    University of Illinois Urbana-Champaign, United States

    4 publications · 428 citations

    4

    h-index

  3. HZ
    Honglei Zhuang

    University of Illinois Urbana-Champaign, United States

    4 publications · 244 citations

    4

    h-index

  4. EH
    Eyke Hüllermeier

    Philipps University of Marburg, Germany

    4 publications · 94 citations

    4

    h-index

  5. CB
    Craig Boutilier

    University of Toronto, Canada

    3 publications · 252 citations

    3

    h-index

1–5 of 651

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 746 citations

    13

    h-index

  2. European Journal of Operational Research

    6 publications · 305 citations

    6

    h-index

  3. ACM Transactions on Information Systems

    4 publications · 135 citations

    4

    h-index

  4. The Annals of Statistics

    4 publications · 142 citations

    4

    h-index

  5. Machine Learning

    2 publications · 105 citations

    3

    h-index

1–5