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Relevance and retrieval evaluation: Perspectives from medicine

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  • William Hersh

Abstract

The traditional notion of topical relevance has allowed much useful work to be done in the evaluation of retrieval systems, but has limitations for complete assessment of retrieval systems. While topical relevance can be effective in evaluating various indexing and retrieval approaches, it is ineffective for measuring the impact that systems have on users. An alternative is to use a more situational definition of relevance, which takes account of the impact of the system on the user. Both types of relevance are examined from the standpoint of the medical domain, concluding that each have their appropriate use. But in medicine there is increasing emphasis on outcomes‐oriented research which, when applied to information science, requires that the impact of an information system on the activities which prompt its use be assessed. An iterative model of retrieval evaluation is proposed, starting first with the use of topical relevance to insure documents on the subject can be retrieved. This is followed by the use of situational relevance to show the user can interact positively with the system. The final step is to study how the system impacts the user in the purpose for which the system was consulted, which can be done by methods such as protocol analysis and simulation. These diverse types of studies are necessary to increase our understanding of the nature of retrieval systems. © 1994 John Wiley & Sons, Inc.

Suggested Citation

  • William Hersh, 1994. "Relevance and retrieval evaluation: Perspectives from medicine," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 45(3), pages 201-206, April.
  • Handle: RePEc:bla:jamest:v:45:y:1994:i:3:p:201-206
    DOI: 10.1002/(SICI)1097-4571(199404)45:33.0.CO;2-W
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    Cited by:

    1. Anton van der Vegt & Guido Zuccon & Bevan Koopman, 2021. "Do better search engines really equate to better clinical decisions? If not, why not?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(2), pages 141-155, February.
    2. Xiaoli Huang & Dagobert Soergel, 2013. "Relevance: An improved framework for explicating the notion," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 18-35, January.
    3. Pertti Vakkari & Michael Völske & Martin Potthast & Matthias Hagen & Benno Stein, 2021. "Predicting essay quality from search and writing behavior," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(7), pages 839-852, July.
    4. Gineke Wiggers & Suzan Verberne & Wouter van Loon & Gerrit‐Jan Zwenne, 2023. "Bibliometric‐enhanced legal information retrieval: Combining usage and citations as flavors of impact relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(8), pages 1010-1025, August.

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