Skip to main content
SpringerLink
Log in

Effective Pre-retrieval Query Performance Prediction Using Similarity and Variability Evidence

  • Conference paper
Advances in Information Retrieval (ECIR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4956))

Included in the following conference series:

Abstract

Query performance prediction aims to estimate the quality of answers that a search system will return in response to a particular query. In this paper we propose a new family of pre-retrieval predictors based on information at both the collection and document level. Pre-retrieval predictors are important because they can be calculated from information that is available at indexing time; they are therefore more efficient than predictors that incorporate information obtained from actual search results. Experimental evaluation of our approach shows that the new predictors give more consistent performance than previously proposed pre-retrieval methods across a variety of data types and search tasks.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bailey, P., Craswell, N., Hawking, D.: Engineering a multi-purpose test collection for web retrieval experiments. Information Processing and Management 39(6), 853–871 (2003)

    Article  Google Scholar 

  2. Broder, A.: A taxonomy of web search. SIGIR Forum 36(2), 3–10 (2002)

    Article  Google Scholar 

  3. Buckley, C., Voorhees, E.M.: Retrieval system evaluation. In: Voorhees, E.M., Harman, D.K. (eds.) TREC: experiment and evaluation in information retrieval, MIT Press, Cambridge (2005)

    Google Scholar 

  4. Carmel, D., Yom-Tov, E., Soboroff, I.: SIGIR workshop report: predicting query difficulty - methods and applications. SIGIR Forum 39(2), 25–28 (2005)

    Article  Google Scholar 

  5. Clarke, C., Craswell, N., Soboroff, I.: Overview of the TREC, terabyte track. In: The Thirteenth Text REtrieval Conference (TREC 2004), Gaithersburg, MD, 2005. National Institute of Standards and Technology Special Publication 500-261 (2004)

    Google Scholar 

  6. Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting query performance. In: Proceedings of the ACM SIGIR International Conference on Research and Development in Information Retrieval, Tampere, Finland, pp. 299–306 (2005)

    Google Scholar 

  7. Freund, J.E.: Modern Elementary Statistics, 10th edn. (2001)

    Google Scholar 

  8. Harman, D., Buckley, C.: The NRRC reliable information access (RIA) workshop. In: Proceedings of the ACM SIGIR International Conference on Research and Development in Information Retrieval, Sheffield, United Kingdom, pp. 528–529 (2004)

    Google Scholar 

  9. He, B., Ounis, I.: Query performance prediction. Information System 31(7), 585–594 (2006)

    Article  Google Scholar 

  10. Kwok, K.L.: An attempt to identify weakest and strongest queries. In: Predicting Query Difficulty, SIGIR 2005 Workshop (2005)

    Google Scholar 

  11. Scholer, F., Williams, H.E., Turpin, A.: Query association surrogates for web search. Journal of the American Society for Information Science and Technology 55(7), 637–650 (2004)

    Article  Google Scholar 

  12. Sheskin, D.: Handbook of parametric and nonparametric statistical proceedures. CRC Press, Boca Raton (1997)

    Google Scholar 

  13. Sparck Jones, K., Walker, S., Robertson, S.E.: A probabilistic model of information retrieval: development and comparative experiments. Part 1. Information Processing and Management 36(6), 779–808 (2000)

    Article  Google Scholar 

  14. Voorhees, E.M.: Overview of the TREC, robust retrieval track. In: The Fourteenth Text REtrieval Conference (TREC 2005), Gaithersburg, MD, 2006. National Institute of Standards and Technology Special Publication 500-266 (2005)

    Google Scholar 

  15. Witten, I., Moffat, A., Bell, T.: Managing Gigabytes: Compressing and Indexing Documents and Images, 2nd edn. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  16. Yom-Tov, E., Fine, S., Carmel, D., Darlow, A.: Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval. In: Proceedings of the ACM SIGIR International Conference on Research and Development in Information Retrieval, Salvador, Brazil, pp. 512–519 (2005)

    Google Scholar 

  17. Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Transactions On Information Systems 22(2), 179–214 (2004)

    Article  Google Scholar 

  18. Zhou, Y., Croft, W.B.: Ranking robustness: a novel framework to predict query performance. In: Proceedings of the ACM SIGIR International Conference on Research and Development in Information Retrieval, Arlington, Virginia, pp. 567–574 (2006)

    Google Scholar 

  19. Zhou, Y., Croft, W.B.: Query performance prediction in web search environments. In: Proceedings of the ACM SIGIR International Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands, pp. 543–550 (2007)

    Google Scholar 

  20. Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Computing Surveys 38(2) (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and IT, RMIT University, GPO Box 2476v, Melbourne, Australia

    Ying Zhao, Falk Scholer & Yohannes Tsegay

Authors
  1. Ying Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Falk Scholer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yohannes Tsegay
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Craig Macdonald Iadh Ounis Vassilis Plachouras Ian Ruthven Ryen W. White

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhao, Y., Scholer, F., Tsegay, Y. (2008). Effective Pre-retrieval Query Performance Prediction Using Similarity and Variability Evidence. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78646-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78645-0

  • Online ISBN: 978-3-540-78646-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation