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Evaluation of an Adaptive Search Suggestion System

  • Sascha Kriewel
  • Norbert Fuhr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Information Retrieval Search Task Digital Library Search System Relevant Document 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sascha Kriewel
    • 1
  • Norbert Fuhr
    • 1
  1. 1.University of Duisburg–Essen 

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