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Improving retrieval performance by relevance feedback

Author

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  • Gerard Salton
  • Chris Buckley

Abstract

Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate the effectiveness of the various methods. Prescriptions are given for conducting text retrieval operations iteratively using relevance feedback. © 1990 John Wiley & Sons, Inc.

Suggested Citation

  • Gerard Salton & Chris Buckley, 1990. "Improving retrieval performance by relevance feedback," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(4), pages 288-297, June.
    • Handle: RePEc:bla:jamest:v:41:y:1990:i:4:p:288-297
    DOI: 10.1002/(SICI)1097-4571(199006)41:43.0.CO;2-H
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    Cited by:

    1. Roland Graef & Mathias Klier & Kilian Kluge & Jan Felix Zolitschka, 2021. "Human-machine collaboration in online customer service – a long-term feedback-based approach," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 319-341, June.
    2. Asim Roy & Patrick Mackin & Jyrki Wallenius & James Corner & Mark Keith & Gregory Schymik & Hina Arora, 2008. "An Interactive Search Method Based on User Preferences," Decision Analysis, INFORMS, vol. 5(4), pages 203-229, December.
    3. Mariam Daoud & Jimmy Xiangji Huang, 2013. "Modeling geographic, temporal, and proximity contexts for improving geotemporal search," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 190-212, January.
    4. Zhixiang Chen & Bin Fu & John Abraham, 2010. "A quadratic lower bound for Rocchio’s similarity-based relevance feedback algorithm with a fixed query updating factor," Journal of Combinatorial Optimization, Springer, vol. 19(2), pages 134-157, February.
    5. Yousif A. Alhaj & Abdelghani Dahou & Mohammed A. A. Al-qaness & Laith Abualigah & Aaqif Afzaal Abbasi & Nasser Ahmed Obad Almaweri & Mohamed Abd Elaziz & Robertas Damaševičius, 2022. "A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language," Future Internet, MDPI, vol. 14(7), pages 1-18, June.
    6. Hoppenbrouwers, J.J.A.C., 1998. "Advanced conceptual network usage in library database queries," Other publications TiSEM 711b739d-edc9-4f72-8fb1-2, Tilburg University, School of Economics and Management.
    7. Veda C. Storey & Andrew Burton-Jones & Vijayan Sugumaran & Sandeep Purao, 2008. "CONQUER: A Methodology for Context-Aware Query Processing on the World Wide Web," Information Systems Research, INFORMS, vol. 19(1), pages 3-25, March.
    8. Michel Zitt, 2015. "Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2223-2245, March.

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