Advertisement

Evaluating Relevance Feedback and Display Strategies for Searching on Small Displays

  • Vishwa Vinay
  • Ingemar J. Cox
  • Natasa Milic-Frayling
  • Ken Wood
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3246)

Abstract

Searching information resources using mobile devices is affected by displays on which only a small fraction of the set of ranked documents can be displayed. In this study we explore the effectiveness of relevance feedback methods in assisting the user to access a predefined target document through searching on a small display device. We propose an innovative approach to study this problem. For small display size and, thus, limited decision choices for relevance feedback, we generate and study the complete space of user interactions and system responses. This is done by building a tree – the documents displayed at any level depend on the choice of relevant document made at the earlier level. Construction of the tree of all possible user interactions permits an evaluation of relevance feedback algorithms with reduced reliance on user studies. From the point of view of real applications, the first few iterations are most important – we therefore limit ourselves to a maximum depth of six in the tree.

Keywords

Relevance Feedback Initial Query Target Document Small Display Display Strategy 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cox, I.J., Miller, M.L., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: The Bayesian Image Retrieval System, PicHunter: Theory, Implementation and Psychophysical Experiments. IEEE Transactions on Image Processing 9(1), 20–37 (2000)CrossRefGoogle Scholar
  2. 2.
    Harman, D.: Relevance feedback revisited. In: Proceedings of 15th annual international ACM SIGIR conference on research and development in information retrieval, Copenhagen, 1.10, p. 10 (1992)Google Scholar
  3. 3.
    Rocchio, J.: Relevance feedback information retrieval. In: Salton, G. (ed.) The Smart Retrieval System – Experiments in Automatic Document Processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)Google Scholar
  4. 4.
    Robertson, S.E., Sparck Jones, K.: Relevance weighting of search terms. Journal of the American Society for Information Science 27, 129–146 (1976)CrossRefGoogle Scholar
  5. 5.
    Sparck Jones, K., Walker, S., Robertson, S.E.: A probabilistic model of information retrieval: development and comparative experiments. Information Processing and Management 36, 779–808, 809-840 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Vishwa Vinay
    • 1
  • Ingemar J. Cox
    • 1
  • Natasa Milic-Frayling
    • 2
  • Ken Wood
    • 2
  1. 1.Department of Computer ScienceUniversity College LondonUK
  2. 2.Microsoft Research LtdCambridgeUK

Personalised recommendations