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Adrian CHIFU
21 Nov 2020

Query Performance Prediction Focused on Summarized Letor Features

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.


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author = {Chifu, Adrian-Gabriel and Laporte, L\'{e}a and Mothe, Josiane and Ullah, Md Zia},
title = {Query Performance Prediction Focused on Summarized Letor Features},
year = {2018},
isbn = {9781450356572},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3209978.3210121},
doi = {10.1145/3209978.3210121},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {1177–1180},
numpages = {4},
keywords = {query features, post retrieval features, query difficulty prediction, letor features, query performance prediction},
location = {Ann Arbor, MI, USA},
series = {SIGIR '18}