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Query performance prediction focused on summarized letor features

Chifu, Adrian-Gabriel; Laporte, Léa; Mothe, Josiane; Ullah, Md Zia

Authors

Adrian-Gabriel Chifu

Léa Laporte

Josiane Mothe



Abstract

Query performance prediction (QPP) aims at automatically estimating the information retrieval system effectiveness for any user's query. Previous work has investigated several types of pre- and post-retrieval query performance predictors; the latter has been shown to be more effective. In this paper we investigate the use of features that were initially defined for learning to rank in the task of QPP. While these features have been shown to be useful for learning to rank documents, they have never been studied as query performance predictors. We developed more than 350 variants of them based on summary functions. Conducting experiments on four TREC standard collections, we found that Letor-based features appear to be better QPP than predictors from the literature. Moreover, we show that combining the best Letor features outperforms the state of the art query performance predictors. This is the first study that considers such an amount and variety of Letor features for QPP and that demonstrates they are appropriate for this task.

Presentation Conference Type Conference Paper (Published)
Conference Name SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
Start Date Jul 8, 2018
End Date Jul 12, 2018
Online Publication Date Jun 27, 2018
Publication Date 2018-06
Deposit Date Mar 13, 2023
Publisher Association for Computing Machinery (ACM)
Pages 1177-1180
Book Title SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
ISBN 978-1-4503-5657-2
DOI https://doi.org/10.1145/3209978.3210121