Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Precision and Recall

  • Ben CarteretteEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_5050


False negative rate; Positive predictive value; Sensitivity


Recall measures the ability of a search engine or retrieval system to locate relevant material in its index. Precision measures its ability to not rank nonrelevant material. With everything above rank cut-off n considered “retrieved” and everything below considered “not retrieved,” precision and recall can be stated mathematically as:
$$ \begin{array}{l}\mathrm{precision}=\frac{\left|\mathrm{retrieved}\kern0.5em \&\kern0.5em \mathrm{relevant}\kern0.5em \mathrm{at}\kern0.5em \mathrm{rank}\kern0.5em n\right|}{\left|\mathrm{retrieved}\kern0.5em \mathrm{at}\kern0.5em \mathrm{rank}\kern0.5em n\right|}\\[8pt] {}\mathrm{recall}=\frac{\left|\mathrm{retrieved}\kern0.5em \&\kern0.5em \mathrm{relevant}\kern0.5em \mathrm{at}\kern0.5em \mathrm{rank}\kern0.5em n\right|}{\left|\mathrm{relevant}\right|}\end{array} $$
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Recommended Reading

  1. 1.
    van Rijsbergen CJ. Information retrieval. London: Butterworths; 1979.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Massachusetts AmherstAmherstUSA