Evaluation of an Adaptive Search Suggestion System

  • Sascha Kriewel
  • Norbert Fuhr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5993)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aula, A., Nordhausen, K.: Modeling successful performance in web searching. Journal of the American Society for Information Science and Technology 57(12), 1678–1693 (2006)CrossRefGoogle Scholar
  2. 2.
    Awasum, M.: Suggestions for Google websearch using a firefox add–on. bachelor thesis, University of Duisburg-Essen (2008)Google Scholar
  3. 3.
    Bates, M.J.: Information search tactics. Journal of the American Society for Information Science 30(4), 205–214 (1979)CrossRefGoogle Scholar
  4. 4.
    Bates, M.J.: Where should the person stop and the information search interface start? Information Processing and Management 26(5), 575–591 (1990)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Belkin, N.J., Cool, C., Stein, A., Thiel, U.: Cases, scripts, and information-seeking strategies: On the design of interactive information retrieval systems. Expert Systems with Applications 9(3), 379–395 (1995)CrossRefGoogle Scholar
  6. 6.
    Belkin, N.J., Marchetti, P.G., Cool, C.: BRAQUE: Design of an interface to support user interaction in information retrieval. Information Processing and Management 29(3), 325–344 (1993)CrossRefGoogle Scholar
  7. 7.
    Bhavnani, S.K., Christopher, B.K., Johnson, T.M., Little, R.J., Peck, F.A., Schwartz, J.L., Strecher, V.J.: Strategy hubs: next-generation domain portals with search procedures. In: Proceedings of the conference on Human factors in computing systems, pp. 393–400. ACM Press, New York (2003)CrossRefGoogle Scholar
  8. 8.
    Brajnik, G., Mizzaro, S., Tasso, C., Venuti, F.: Strategic help in user interfaces for information retrieval. Journal of the American Society for Information Science and Technology 53(5), 343–358 (2002)CrossRefGoogle Scholar
  9. 9.
    Carstens, C., Rittberger, M., Wissel, V.: How users search in the german education index - tactics and strategies. In: Proceedings of the workshop Information Retrieval at the LWA 2009 (2009)Google Scholar
  10. 10.
    Drabenstott, K.M.: Do nondomain experts enlist the strategies of domain experts. Journal of the American Society for Information Science and Technology 54(9), 836–854 (2003)CrossRefGoogle Scholar
  11. 11.
    Fields, B., Keith, S., Blandford, A.: Designing for expert information finding strategies. In: Fincher, S., Markopoulos, P., Moore, D., Ruddle, R.A. (eds.) BCS HCI, pp. 89–102. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Fuhr, N., Klas, C.-P., Schaefer, A., Mutschke, P.: Daffodil: An integrated desktop for supporting high-level search activities in federated digital libraries. In: Agosti, M., Thanos, C. (eds.) ECDL 2002. LNCS, vol. 2458, pp. 597–612. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Harter, S.P.: Online information retrieval: concepts, principles, and techniques. Academic Press Professional, Inc., San Diego (1986)Google Scholar
  14. 14.
    Harter, S.P., Peters, A.R.: Heuristics for online information retrieval: a typology and preliminary listing. Online Review 9(5), 407–424 (1985)CrossRefGoogle Scholar
  15. 15.
    Jansen, B.J.: Seeking and implementing automated assistance during the search process. Information Processing and Management 41(4), 909–928 (2005)CrossRefGoogle Scholar
  16. 16.
    Jansen, B.J., McNeese, M.D.: Evaluating the effectiveness of and patterns of interactions with automated searching assistance. Journal of the American Society for Information Science and Technology 56(14), 1480–1503 (2005)CrossRefGoogle Scholar
  17. 17.
    Järvelin, K.: Explaining user performance in information retrieval: Challenges to ir evaluation. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 289–296. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  18. 18.
    Klas, C.-P., Albrechtsen, H., Fuhr, N., Hansen, P., Kapidakis, S., Kovács, L., Kriewel, S., Micsik, A., Papatheodorou, C., Tsakonas, G., Jacob, E.: A logging scheme for comparative digital library evaluation. In: Gonzalo, J., Thanos, C., Verdejo, M.F., Carrasco, R.C. (eds.) ECDL 2006. LNCS, vol. 4172, pp. 267–278. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Klas, C.-P., Fuhr, N., Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system. In: Heery, R., Lyon, L. (eds.) ECDL 2004. LNCS, vol. 3232, pp. 476–487. Springer, Heidelberg (2004)Google Scholar
  20. 20.
    Kriewel, S., Fuhr, N.: Adaptive search suggestions for digital libraries. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 220–229. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Markey, K.: Twenty-five years of end-user searching, part 1: Research findings. Journal of the American Society for Information Science and Technology 58(8), 1071–1081 (2007)CrossRefGoogle Scholar
  22. 22.
    Ontañón, S., Plaza, E.: Justification-based multiagent learning. In: Mishra, N., Fawcett, T. (eds.) The Twentieth International Conference on Machine Learning (ICML 2003), pp. 576–583. AAAI Press, Menlo Park (2003)Google Scholar
  23. 23.
    Pollock, A., Hockley, A.: What’s wrong with internet searching. D-Lib Magazine (March 1997)Google Scholar
  24. 24.
    Rieh, S.Y., Xie, H.(I.): Patterns and sequences of multiple query reformulations in web searching: a preliminary study. In: Proceedings of the 64th Annual Meeting of the American Society for Information Science and Technology, vol. 38, pp. 246–255 (2001)Google Scholar
  25. 25.
    Schaefer, A., Jordan, M., Klas, C.-P., Fuhr, N.: Active support for query formulation in virtual digital libraries: A case study with DAFFODIL. In: Rauber, A., Christodoulakis, S., Tjoa, A.M. (eds.) ECDL 2005. LNCS, vol. 3652, pp. 414–425. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  26. 26.
    Wildemuth, B.M.: The effects of domain knowledge on search tactic formulation. Journal of the American Society for Information Science and Technology 55(3), 246–258 (2004)CrossRefGoogle Scholar
  27. 27.
    Xie, H.I.: Shifts of interactive intentions and information-seeking strategies in interactive information retrieval. Journal of the American Society for Information Science 51(9), 841–857 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

Personalised recommendations