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On selecting a measure of retrieval effectiveness

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  • William S. Cooper

Abstract

It is argued that a user's subjective evaluation of the personal utility of a retrieval system's output to him, if it could be properly quantified, would be a near‐ideal measure of retrieval effectiveness. A hypothetical methodology is presented for measuring this utility by means of an elicitation procedure. Because the hypothetical methodology is impractical, compromise methods are outlined and their underlying simplifying assumptions are discussed. The more plausible the simplifying assumptions on which a performance measure is based, the better the measure. This, along with evidence gleaned from ‘validation experiments’ of a certain kind, is suggsted as a criterion for selecting or deriving the best measure of effectiveness to use under given test conditions.

Suggested Citation

  • William S. Cooper, 1973. "On selecting a measure of retrieval effectiveness," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(2), pages 87-100, March.
    • Handle: RePEc:bla:jamest:v:24:y:1973:i:2:p:87-100
    DOI: 10.1002/asi.4630240204
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    Cited by:

    1. Wayne de Fremery & Michael K. Buckland, 2022. "Context, relevance, and labor," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(9), pages 1268-1278, September.
    2. Yonit Rusho & Daphne R. Raban, 2020. "Hands on: Information Experiences as Sources of Value," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(6), pages 671-684, June.
    3. 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.
    4. Azzah Al‐Maskari & Mark Sanderson, 2010. "A review of factors influencing user satisfaction in information retrieval," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(5), pages 859-868, May.
    5. Kevin W Boyack & David Newman & Russell J Duhon & Richard Klavans & Michael Patek & Joseph R Biberstine & Bob Schijvenaars & André Skupin & Nianli Ma & Katy Börner, 2011. "Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.

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