Query performance prediction (QPP) aims to automatically estimate the performance of a query. Recently there have been many attempts to use these predictors to estimate whether a perturbed version of a query will outperform the original version. In essence, these approaches attempt to navigate the space of queries in a guided manner. In this paper, we perform an analysis of the query space over a substantial number of queries and show that (1) users tend to be able to extract queries that perform in the top 5% of all possible user queries for a specific topic, (2) that post-retrieval predictors outperform pre-retrieval predictors at the high end of the query space. And, finally (3), we show that some post retrieval predictors are better able to select high performing queries from a group of user queries for the same topic. © 2011 Springer-Verlag.
CITATION STYLE
Cummins, R., Lalmas, M., O’Riordan, C., & Jose, J. M. (2011). Navigating the user query space. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7024 LNCS, pp. 380–385). https://doi.org/10.1007/978-3-642-24583-1_37
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