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

  • Ying Zhao
  • Falk Scholer
  • Yohannes Tsegay
Conference paper

DOI: 10.1007/978-3-540-78646-7_8

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4956)
Cite this paper as:
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

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ying Zhao
    • 1
  • Falk Scholer
    • 1
  • Yohannes Tsegay
    • 1
  1. 1.School of Computer Science and ITRMIT UniversityMelbourneAustralia

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