Probabilistic Retrieval

Probabilistic Retrieval is an information retrieval framework that ranks documents based on the estimated probability of relevance to a given query, distinguishing itself from other retrieval models by employing probabilistic theories to quantify uncertainty in document relevance.

1.1K

Publications

100.5K

Citations

2K

Authors

566

Institutions

Publications per year

2017–2026

88

Authors

2K

  1. WB
    W. Bruce Croft

    University of Massachusetts Amherst, United States

    31 publications · 7.3K citations

    25

    h-index

  2. SR
    Stephen Robertson

    Microsoft Research (United Kingdom), United Kingdom

    24 publications · 10.2K citations

    18

    h-index

  3. NF
    Norbert Fuhr

    University of Duisburg-Essen, Germany

    21 publications · 1.7K citations

    18

    h-index

  4. CZ
    ChengXiang Zhai

    University of Illinois Urbana-Champaign, United States

    17 publications · 3.1K citations

    16

    h-index

  5. MD
    Maarten de Rijke

    University of Amsterdam, Netherlands

    18 publications · 598 citations

    14

    h-index

1–5 of 2K

Venues

  1. ACM Transactions on Information Systems

    36 publications · 4.8K citations

    27

    h-index

  2. Information Processing & Management

    43 publications · 3.6K citations

    26

    h-index

  3. Journal of the American Society for Information Science

    JASIS · 37 publications · 3.8K citations

    22

    h-index

  4. ACM SIGIR Forum

    14 publications · 7.9K citations

    13

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 12 publications · 713 citations

    12

    h-index

1–5