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Evaluating the effectiveness of and patterns of interactions with automated searching assistance

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  • Bernard J. Jansen
  • Michael D. McNeese

Abstract

We report quantitative and qualitative results of an empirical evaluation to determine whether automated assistance improves searching performance and when searchers desire system intervention in the search process. Forty participants interacted with two fully functional information retrieval systems in a counterbalanced, within‐participant study. The systems were identical in all respects except that one offered automated assistance and the other did not. The study used a client‐side automated assistance application, an approximately 500,000‐document Text REtrieval Conference content collection, and six topics. Results indicate that automated assistance can improve searching performance. However, the improvement is less dramatic than one might expect, with an approximately 20% performance increase, as measured by the number of user‐selected relevant documents. Concerning patterns of interaction, we identified 1,879 occurrences of searcher– system interactions and classified them into 9 major categories and 27 subcategories or states. Results indicate that there are predictable patterns of times when searchers desire and implement searching assistance. The most common three‐state pattern is Execute Query–View Results: With Scrolling–View Assistance. Searchers appear receptive to automated assistance; there is a 71% implementation rate. There does not seem to be a correlation between the use of assistance and previous searching performance. We discuss the implications for the design of information retrieval systems and future research directions.

Suggested Citation

  • Bernard J. Jansen & Michael D. McNeese, 2005. "Evaluating the effectiveness of and patterns of interactions with automated searching assistance," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(14), pages 1480-1503, December.
  • Handle: RePEc:bla:jamist:v:56:y:2005:i:14:p:1480-1503
    DOI: 10.1002/asi.20242
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    Cited by:

    1. Jahangirian, Mohsen & Eldabi, Tillal & Garg, Lalit & Jun, Gyuchan T. & Naseer, Aisha & Patel, Brijesh & Stergioulas, Lampros & Young, Terry, 2011. "A rapid review method for extremely large corpora of literature: Applications to the domains of modelling, simulation, and management," International Journal of Information Management, Elsevier, vol. 31(3), pages 234-243.

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