Skip to main content

Evaluating Relevance Feedback and Display Strategies for Searching on Small Displays

  • Conference paper
String Processing and Information Retrieval (SPIRE 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3246))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  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. 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. Robertson, S.E., Sparck Jones, K.: Relevance weighting of search terms. Journal of the American Society for Information Science 27, 129–146 (1976)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vinay, V., Cox, I.J., Milic-Frayling, N., Wood, K. (2004). Evaluating Relevance Feedback and Display Strategies for Searching on Small Displays. In: Apostolico, A., Melucci, M. (eds) String Processing and Information Retrieval. SPIRE 2004. Lecture Notes in Computer Science, vol 3246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30213-1_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30213-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23210-0

  • Online ISBN: 978-3-540-30213-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics