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Towards an adaptive information retrieval system

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Methodologies for Intelligent Systems (ISMIS 1991)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 542))

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Abstract

Standard Information Retrieval Systems (IRS) can be used to retrieve information in response to specific requests, but they have no powers of adaption to particular users over repeated sessions. This paper describes a learning system which uses relevance feedback from a probabilistic IRS to incrementally evolve a context for a user, over a number of online sessions. We demonstrate the learning implementation with an example, and argue that it can help an IRS adapt to a user's specific needs, by using this context to influence document display and selection.

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References

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Author information

Authors and Affiliations

  1. Department of Information Science, City University, Northampton Square, EC1V OHB, London, UK

    A. Goker

  2. Department of Computer Science, City University, Northampton Square, EC1V OHB, London, UK

    T. L. McCluskey

Authors
  1. A. Goker
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  2. T. L. McCluskey
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Editor information

Z. W. Ras M. Zemankova

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© 1991 Springer-Verlag Berlin Heidelberg

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Goker, A., McCluskey, T.L. (1991). Towards an adaptive information retrieval system. In: Ras, Z.W., Zemankova, M. (eds) Methodologies for Intelligent Systems. ISMIS 1991. Lecture Notes in Computer Science, vol 542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54563-8_98

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  • DOI: https://doi.org/10.1007/3-540-54563-8_98

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54563-7

  • Online ISBN: 978-3-540-38466-3

  • eBook Packages: Springer Book Archive

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