Pairwise Preference Modeling

Pairwise Preference Modeling is a methodological framework in information retrieval that, as a subset of preference learning, ranks items by systematically comparing pairs of documents to determine their relative relevance with respect to a query.

233

Publications

11.4K

Citations

595

Authors

295

Institutions

Publications per year

2017–2026

55

Authors

595

  1. SS
    Stephen Stark

    University of South Florida, United States

    8 publications · 429 citations

    8

    h-index

  2. OS
    Oleksandr S. Chernyshenko

    Nanyang Technological University, Singapore

    7 publications · 415 citations

    7

    h-index

  3. WP
    Weike Pan

    Shenzhen University, China

    5 publications · 302 citations

    5

    h-index

  4. SO
    Sewoong Oh

    University of Illinois Urbana-Champaign, United States

    5 publications · 449 citations

    5

    h-index

  5. SN
    Sahand Negahban

    University of California, Berkeley, United States

    5 publications · 433 citations

    5

    h-index

1–5 of 595

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 422 citations

    11

    h-index

  2. European Journal of Operational Research

    10 publications · 729 citations

    10

    h-index

  3. Knowledge-Based Systems

    4 publications · 81 citations

    4

    h-index

  4. Journal of Mathematical Psychology

    4 publications · 200 citations

    4

    h-index

  5. Applied Psychological Measurement

    4 publications · 327 citations

    4

    h-index

1–5