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Finding relevant search engines results: a minimax linear programming approach

Author

Listed:
  • G R Amin

    (Islamic Azad University of South Tehran Branch)

  • A Emrouznejad

    (Aston University)

Abstract

For a submitted query to multiple search engines finding relevant results is an important task. This paper formulates the problem of aggregation and ranking of multiple search engines results in the form of a minimax linear programming model. Besides the novel application, this study detects the most relevant information among a return set of ranked lists of documents retrieved by distinct search engines. Furthermore, two numerical examples aree used to illustrate the usefulness of the proposed approach.

Suggested Citation

  • G R Amin & A Emrouznejad, 2010. "Finding relevant search engines results: a minimax linear programming approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(7), pages 1144-1150, July.
    • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:7:d:10.1057_jors.2009.53
    DOI: 10.1057/jors.2009.53
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    Cited by:

    1. Gao, Jianwei & Li, Ming & Liu, Huihui, 2015. "Generalized ordered weighted utility averaging-hyperbolic absolute risk aversion operators and their applications to group decision-making," European Journal of Operational Research, Elsevier, vol. 243(1), pages 258-270.
    2. Gao, Jianwei & Li, Ming & Liu, Huihui, 2015. "Generalized ordered weighted utility proportional averaging-hyperbolic absolute risk aversion operators and their applications to group decision-making," Applied Mathematics and Computation, Elsevier, vol. 252(C), pages 114-132.

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