Query performance prediction: evaluation contrasted with effectiveness

Hauff, C., Azzopardi, L., Hiemstra, D. and de Jong, F. (2010) Query performance prediction: evaluation contrasted with effectiveness. Lecture Notes in Computer Science, 5993, pp. 204-216. (doi: 10.1007/978-3-642-12275-0_20)

Abstract

Query performance predictors are commonly evaluated by reporting correlation coefficients to denote how well the methods perform at predicting the retrieval performance of a set of queries. Despite the amount of research dedicated to this area, one aspect remains neglected: how strong does the correlation need to be in order to realize an improvement in retrieval effectiveness in an operational setting? We address this issue in the context of two settings: Selective Query Expansion and Meta-Search. In an empirical study, we control the quality of a predictor in order to examine how the strength of the correlation achieved, affects the effectiveness of an adaptive retrieval system. The results of this study show that many existing predictors fail to achieve a correlation strong enough to reliably improve the retrieval effectiveness in the Selective Query Expansion as well as the Meta-Search setting.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Azzopardi, Dr Leif
• Authors: Hauff, C., Azzopardi, L., Hiemstra, D., and de Jong, F.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Lecture Notes in Computer Science
• ISSN: 0302-9743
• ISSN (Online): 1611-3349