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Using the cross-entropy method to re-rank search results

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SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 839 - 842

https://doi.org/10.1145/2600428.2609454

Published: 03 July 2014 Publication History

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Abstract

We present a novel unsupervised approach to re-ranking an initially retrieved list. The approach is based on the Cross Entropy method applied to permutations of the list, and relies on performance prediction. Using pseudo predictors we establish a lower bound on the prediction quality that is required so as to have our approach significantly outperform the original retrieval. Our experiments serve as a proof of concept demonstrating the considerable potential of the proposed approach. A case in point, only a tiny fraction of the huge space of permutations needs to be explored to attain significant improvements over the original retrieval.

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Cited By

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Zamani HBendersky MMetzler DZhuang HWang XCrestani FPasi GGaussier E(2022)Stochastic Retrieval-Conditioned RerankingProceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3539813.3545141(81-91)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1145/3539813.3545141
Roitman HBalog KSetty VLioma CLiu YZhang MBerberich K(2020)ICTIR Tutorial: Modern Query Performance Prediction: Theory and PracticeProceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval10.1145/3409256.3409813(195-196)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1145/3409256.3409813
Penha GCampos RCanuto SGonçalves MSantos R(2019)Document Performance Prediction for Automatic Text ClassificationAdvances in Information Retrieval10.1007/978-3-030-15719-7_17(132-139)Online publication date: 7-Apr-2019
https://doi.org/10.1007/978-3-030-15719-7_17
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Index Terms

Using the cross-entropy method to re-rank search results
1. Information systems
  1. Information retrieval

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Published In

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2014

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SIGIR '14

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SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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Zamani HBendersky MMetzler DZhuang HWang XCrestani FPasi GGaussier E(2022)Stochastic Retrieval-Conditioned RerankingProceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3539813.3545141(81-91)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1145/3539813.3545141
Roitman HBalog KSetty VLioma CLiu YZhang MBerberich K(2020)ICTIR Tutorial: Modern Query Performance Prediction: Theory and PracticeProceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval10.1145/3409256.3409813(195-196)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1145/3409256.3409813
Penha GCampos RCanuto SGonçalves MSantos R(2019)Document Performance Prediction for Automatic Text ClassificationAdvances in Information Retrieval10.1007/978-3-030-15719-7_17(132-139)Online publication date: 7-Apr-2019
https://doi.org/10.1007/978-3-030-15719-7_17
Roitman HRabinovich ESar Shalom OLee DSastry NWeber I(2018)As Stable As You AreProceedings of the 29th on Hypertext and Social Media10.1145/3209542.3209567(33-37)Online publication date: 3-Jul-2018
https://dl.acm.org/doi/10.1145/3209542.3209567

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Image re-ranking and rank aggregation based on similarity of ranked lists