Recall Thresholds

Recall Thresholds is a formal metric in information retrieval and machine learning that specifies the minimum proportion of relevant instances a system must retrieve to be considered effective, distinguishing it from other performance thresholds by focusing exclusively on the completeness of retrieved results relative to the total available relevant set.

26

Publications

1.3K

Citations

84

Authors

54

Institutions

Publications per year

2017–2026

6

Authors

84

  1. SR
    Stephen Robertson

    Microsoft Research (United Kingdom), United Kingdom

    2 publications · 99 citations

    2

    h-index

  2. SJ
    Samantha J. Colosimo

    Aberdeen Royal Infirmary, United Kingdom

    1 publications · 52 citations

    1

    h-index

  3. DJ
    Douglas J. Herrmann

    Hamilton College, United States

    1 publications · 27 citations

    1

    h-index

  4. YR
    Yves Robert

    École Normale Supérieure de Lyon, France

    1 publications · 12 citations

    1

    h-index

  5. SM
    Sanjay Manohar

    University of Oxford, United Kingdom

    1 publications · 37 citations

    1

    h-index

1–5 of 84

Venues

  1. Journal of Experimental Psychology

    2 publications · 96 citations

    2

    h-index

  2. Journal of Documentation

    2 publications · 99 citations

    2

    h-index

  3. Journal of Mathematical Psychology

    2 publications · 47 citations

    2

    h-index

  4. 2

    h-index

  5. Neural Networks

    1 publications · 15 citations

    1

    h-index

1–5