Abstract
In this paper we introduce a novel approach for query performance prediction based on ranking list scores dispersion. Starting from the hypothesis that different score distributions appear for good and poor performance queries, we introduce a set of measures that capture these differences between both types of distributions. The use of measures based on standard deviation of ranking list scores, as a prediction value, shows a significant correlation degree in terms of average precision.
This work has been partially supported by the Regional Government of Madrid under the Research Network MAVIR (S-0505/TIC-0267) and the Spanish MICINN project TIN2007-68083.
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Pérez-Iglesias, J., Araujo, L. (2009). Ranking List Dispersion as a Query Performance Predictor. In: Azzopardi, L., et al. Advances in Information Retrieval Theory. ICTIR 2009. Lecture Notes in Computer Science, vol 5766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04417-5_42
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DOI: https://doi.org/10.1007/978-3-642-04417-5_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04416-8
Online ISBN: 978-3-642-04417-5
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