Inferring Query Performance Using Pre-retrieval Predictors

  • Ben He
  • Iadh Ounis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3246)


The prediction of query performance is an interesting and important issue in Information Retrieval (IR). Current predictors involve the use of relevance scores, which are time-consuming to compute. Therefore, current predictors are not very suitable for practical applications. In this paper, we study a set of predictors of query performance, which can be generated prior to the retrieval process. The linear and non-parametric correlations of the predictors with query performance are thoroughly assessed on the TREC disk4 and disk5 (minus CR) collections. According to the results, some of the proposed predictors have significant correlation with query performance, showing that these predictors can be useful to infer query performance in practical applications.


Average Precision Query Term Query Expansion Information Retrieval System Relevance Score 
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.
    Allan, J., Ballesteros, L., Callan, J., Croft, W.: Recent experiments with INQUERY. In: Proceedings of TREC-4, Gaithersburg, MD, pp. 49–63 (1995)Google Scholar
  2. 2.
    Amati, G., Carpineto, C., Romano, G.: Query difficulty, robustness, and selective application of query expansion. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 127–137. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Amati, G., van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring the divergence from randomness. TOIS 20(4), 357–389 (2002)CrossRefGoogle Scholar
  4. 4.
    Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting query performance. In: Proceedings of SIGIR 2002,Tampere, Finland, pp. 299–306 (2002)Google Scholar
  5. 5.
    DeGroot, M.: Probability and Statistics, 2nd edn. Addison Wesley, Reading (1989)Google Scholar
  6. 6.
    Gibbons, J.D., Chakraborti, S.: Nonparametric statistical inference. M. Dekker, New York (1992)zbMATHGoogle Scholar
  7. 7.
    He, B., Ounis, I.: A study of parameter tuning for term frequency normalization. In: Proceedings of CIKM 2003, New Orleans, LA, pp. 10–16 (2003)Google Scholar
  8. 8.
    He, B., Ounis, I.: A query-based pre-retrieval model selection approach to information retrieval. In: Proceedings of RIAO 2004, Avignon, France, pp. 706–719 (2004)Google Scholar
  9. 9.
    Pirkola, A., Jarvelin, K.: Employing the resolution power of search keys. JASIST 52(7), 575–583 (2001)CrossRefGoogle Scholar
  10. 10.
    Plachouras, V., Ounis, I., Amati, G., van Rijsbergen, C.J.: University of Glasgow at the Web Track: Dynamic application of hyperlink analysis using the query scope. In: Proceedings of TREC 2003, Gaithersburg, MD, pp. 248–254 (2003)Google Scholar
  11. 11.
    Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of SIGIR 1998, Melbourne, Australia, pp. 275–281 (1998)Google Scholar
  12. 12.
    Robertson, S., Walker, S., Beaulieu, M.M., Gatford, M., Payne, A.: Okapi at TREC-4. In: Proceedings of TREC-4, Gaithersburg, MD, pp. 73–96 (1995)Google Scholar
  13. 13.
    Song, F., Croft, W.: A general language model for information retrieval. In: Proceedings of SIGIR 1999, Berkeley, CA, pp. 279–280 (1999)Google Scholar
  14. 14.
    Sparck-Jones, K., Walker, S., Robertson, S.: A probabilistic model of information retrieval: Development and comparative experiments. IPM 36(2000), 779–840 (2000)Google Scholar
  15. 15.
    Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proceedings of SIGIR 2001, New Orleans, LA, pp. 334–342 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ben He
    • 1
  • Iadh Ounis
    • 1
  1. 1.Department of Computing ScienceUniversity of Glasgow 

Personalised recommendations