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Adaptive Search Suggestions for Digital Libraries

  • Sascha Kriewel
  • Norbert Fuhr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4822)

Abstract

In this paper, an adaptive tool for providing suggestions during the information search process is presented. The tool uses case-based reasoning techniques to find the most useful suggestions for a given situation by comparing them to a case base of previous situations and adapting the solution. The tool can learn from user participation.

A small, preliminary evaluation showed a high acceptance of the tool, even if improvements are still needed.

Keywords

Search Task Digital Library Information Retrieval System Current Query Context Menu 
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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References

  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–59 (1994)Google Scholar
  2. 2.
    Bates, M.J.: Information search tactics. Journal of the American Society for Information Science 30(4), 205–214 (1979)CrossRefGoogle Scholar
  3. 3.
    Bates, M.J.: The design of browsing and berrypicking techniques for the online search interface. Online Review 13(5), 407–424 (1989)Google 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.: Interaction with texts: Information retrieval as information seeking behavior. In: Knorz, G., Krause, J., Womser-Hacker, C. (eds.) Information Retrieval 1993. Von der Modellierung zur Anwendung. Konstanz, pp. 55–66 (1993)Google Scholar
  6. 6.
    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, 1–30 (1995)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Bhavnani, S.K., Drabenstott, K., Radev, D.: Towards a unified framework of IR tasks and strategies. In: Proceedings of ASIST 2001: 64th Annual Meeting, pp. 340–354 (2001)Google Scholar
  9. 9.
    Brajnik, G., Mizzaro, S., Tasso, C.: Evaluating user interfaces to information retrieval systems: A case study on user support. In: Proceedings of the SIGIR 1996, pp. 128–136 (1996)Google Scholar
  10. 10.
    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
  11. 11.
    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
  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.
    Gövert, N., Fuhr, N., Klas, C.-P.: Daffodil: Distributed agents for user-friendly access of digital libraries. In: Borbinha, J.L., Baker, T. (eds.) ECDL 2000. LNCS, vol. 1923, pp. 352–355. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Hsieh-Yee, I.: Effects of search experience and subject knowledge on online search behavior: Measuring the search tactics of novice and experienced searchers. Journal of the American Society for Information Science 44(3), 161–174 (1993)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.
    Klas, C.-P., Albrechtsen, H., Fuhr, N., Hansen, P., Kapidakis, S., ó 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
  17. 17.
    Kriewel, S.: Finding and using strategies for search situations in digital libraries. Bulletin of the IEEE Technical Committee on Digital Libraries 2(2) (2006), http://www.ieee-tcdl.org/Bulletin/v2n2/kriewel/kriewel.html
  18. 18.
    Kriewel, S., Klas, C.-P., Schaefer, A., Fuhr, N.: Daffodil - strategic support for user-oriented access to heterogeneous digital libraries. D-Lib Magazine 10(6) (June 2004), http://www.dlib.org/dlib/june04/kriewel/06kriewel.html
  19. 19.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

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

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