A unified framework for post-retrieval query-performance prediction

Oren Kurland, Anna Shtok, David Carmel, Shay Hummel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The query-performance prediction task is estimating the effectiveness of a search performed in response to a query in lack of relevance judgments. Post-retrieval predictors analyze the result list of top-retrieved documents. While many of these previously proposed predictors are supposedly based on different principles, we show that they can actually be derived from a novel unified prediction framework that we propose. The framework is based on using a pseudo effective and/or ineffective ranking as reference comparisons to the ranking at hand, the quality of which we want to predict. Empirical exploration provides support to the underlying principles, and potential merits, of our framework.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval Theory - Third International Conference, ICTIR 2011, Proceedings
Number of pages12
StatePublished - 2011
Event3rd International Conference on the Theory of Information Retrieval, ICTIR 2011 - Bertinoro, Italy
Duration: 12 Sep 201114 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6931 LNCS


Conference3rd International Conference on the Theory of Information Retrieval, ICTIR 2011


  • post-retrieval prediction framework
  • query-performance prediction

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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