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Enhancing information source selection using a genetic algorithm and social tagging

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  • Lebib, Fatma Zohra
  • Mellah, Hakima
  • Drias, Habiba

Abstract

The selection of information sources in a distributed information retrieval environment remains a critical issue. In this context, it is known that a distributed information retrieval system consists of a huge number of sources. Ensuring retrieval effectiveness is to search only sources which are likely to contain relevant information for a query. An important number of heuristics exist among which we quote genetic algorithm that is used to solve the above problem. The proposed genetic algorithm consists in finding the best selection in large space of potential solutions; where a solution is represented as a combination of a set of sources. The improvement of selection accuracy is assured based on the user’s track through the use of sources, to say that source description is enriched with tags from the tagging history.

Suggested Citation

  • Lebib, Fatma Zohra & Mellah, Hakima & Drias, Habiba, 2017. "Enhancing information source selection using a genetic algorithm and social tagging," International Journal of Information Management, Elsevier, vol. 37(6), pages 741-749.
    • Handle: RePEc:eee:ininma:v:37:y:2017:i:6:p:741-749
    DOI: 10.1016/j.ijinfomgt.2017.07.011
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    References listed on IDEAS

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    1. Hsinchun Chen & Yi‐Ming Chung & Marshall Ramsey & Christopher C. Yang, 1998. "A smart itsy bitsy spider for the Web," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(7), pages 604-618, May.
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    Cited by:

    1. Taneja, Anu & Arora, Anuja, 2019. "Modeling user preferences using neural networks and tensor factorization model," International Journal of Information Management, Elsevier, vol. 45(C), pages 132-148.

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