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European Conference on Information Retrieval

ECIR 2010: Advances in Information Retrieval pp 544–555Cite as

Evaluation of an Adaptive Search Suggestion System

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5993))

Abstract

This paper describes an adaptive search suggestion system based on case–based reasoning techniques, and details an evaluation of its usefulness in helping users employ better search strategies. A user experiment with 24 participants was conducted using a between–subjects design. One group received search suggestions for the first two out of three tasks, while the other didn’t. Results indicate a correlation between search success, expressed as number of relevant documents saved, and use of suggestions. In addition, users who received suggestions used significantly more of the advanced tools and options of the search system — even after suggestions were switched off during a later task.

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Author information

Authors and Affiliations

  1. University of Duisburg–Essen,  

    Sascha Kriewel & Norbert Fuhr

Authors
  1. Sascha Kriewel
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  2. Norbert Fuhr
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Editor information

Editors and Affiliations

  1. Adaptive Information Cluster, Dublin City University, Dublin, 9, Ireland

    Cathal Gurrin

  2. The Open University, Walton Hall, MK7 6HF, Milton Keynes, UK

    Yulan He

  3. Microsoft Research Ltd, 7 JJ Thomson Avenue, CB3 0FB, Cambridge, UK

    Gabriella Kazai

  4. Department of Computer Science, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Udo Kruschwitz

  5. The Open University, Walton Hall, Milton Keynes, UK

    Suzanne Little

  6. University of London, London, UK

    Thomas Roelleke

  7. Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, UK

    Stefan Rüger

  8. Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, G12 8QQ, Glasgow, UK

    Keith van Rijsbergen

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© 2010 Springer-Verlag Berlin Heidelberg

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Cite this paper

Kriewel, S., Fuhr, N. (2010). Evaluation of an Adaptive Search Suggestion System. In: Gurrin, C., et al. Advances in Information Retrieval. ECIR 2010. Lecture Notes in Computer Science, vol 5993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12275-0_47

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  • DOI: https://doi.org/10.1007/978-3-642-12275-0_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12274-3

  • Online ISBN: 978-3-642-12275-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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