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Variations in relevance assessments and the measurement of retrieval effectiveness

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  • Stephen P. Harter

Abstract

The purpose of this article is to bring attention to the problem of variations in relevance assessments and the effects that these may have on measures of retrieval effectiveness. Through an analytical review of the literature, I show that despite known wide variations in relevance assessments in experimental test collections, their effects on the measurement of retrieval performance are almost completely unstudied. I will further argue that what we know about the many variables that have been found to affect relevance assessments under experimental conditions, as well as our new understanding of psychological, situational, user‐based relevance, point to a single conclusion. We can no longer rest the evaluation of information retrieval systems on the assumption that such variations do not significantly affect the measurement of information retrieval performance. A series of thorough, rigorous, and extensive tests is needed, of precisely how, and under what conditions, variations in relevance assessments do, and do not, affect measures of retrieval performance. We need to develop approaches to evaluation that are sensitive to these variations and to human factors and individual differences more generally. Our approaches to evaluation must reflect the real world of real users. © 1996 John Wiley & Sons, Inc.

Suggested Citation

  • Stephen P. Harter, 1996. "Variations in relevance assessments and the measurement of retrieval effectiveness," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(1), pages 37-49, January.
  • Handle: RePEc:bla:jamest:v:47:y:1996:i:1:p:37-49
    DOI: 10.1002/(SICI)1097-4571(199601)47:13.0.CO;2-3
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    Cited by:

    1. Moghadasi, Shiva Imani & Ravana, Sri Devi & Raman, Sudharshan N., 2013. "Low-cost evaluation techniques for information retrieval systems: A review," Journal of Informetrics, Elsevier, vol. 7(2), pages 301-312.
    2. Tenvir Ali & Zeeshan Jhandir & Ingyu Lee & Byung-Won On & Gyu Sang Choi, 2017. "Evaluating Retrieval Effectiveness by Sustainable Rank List," Sustainability, MDPI, vol. 9(7), pages 1-20, July.

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