Document Relevance Modeling

Document Relevance Modeling is a probabilistic framework that characterizes the likelihood of a document satisfying an information need given a query, distinguished from other retrieval models by its foundational reliance on probabilistic assumptions about relevance to estimate the probability of relevance for ranking.

304

Publications

20.6K

Citations

683

Authors

269

Institutions

Publications per year

2017–2026

52

Authors

683

  1. WB
    W. Bruce Croft

    University of Massachusetts Amherst, United States

    9 publications · 749 citations

    9

    h-index

  2. RW
    Ryen W. White

    Microsoft (United States), United States

    7 publications · 586 citations

    7

    h-index

  3. MD
    Maarten de Rijke

    University of Amsterdam, Netherlands

    6 publications · 150 citations

    7

    h-index

  4. PN
    Paul N. Bennett

    Microsoft (United States), United States

    6 publications · 614 citations

    6

    h-index

  5. GD
    Georges Dupret

    Yahoo (United States), United States

    5 publications · 705 citations

    5

    h-index

1–5 of 683

Venues

  1. ACM Transactions on Information Systems

    13 publications · 1.5K citations

    12

    h-index

  2. Information Processing & Management

    8 publications · 169 citations

    8

    h-index

  3. 6

    h-index

  4. Journal of the American Society for Information Science

    JASIS · 5 publications · 155 citations

    5

    h-index

  5. arXiv (Cornell University)

    Repository · 4 publications · 150 citations

    4

    h-index

1–5