Article

The maximum entropy method for analyzing retrieval measures

Authors:

Javed A. Aslam,

Virgiliu PavluAuthors Info & Claims

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 27 - 34

https://doi.org/10.1145/1076034.1076042

Published: 15 August 2005 Publication History

Abstract

We present a model, based on the maximum entropy method, for analyzing various measures of retrieval performance such as average precision, R-precision, and precision-at-cutoffs. Our methodology treats the value of such a measure as a constraint on the distribution of relevant documents in an unknown list, and the maximum entropy distribution can be determined subject to these constraints. For good measures of overall performance (such as average precision), the resulting maximum entropy distributions are highly correlated with actual distributions of relevant documents in lists as demonstrated through TREC data; for poor measures of overall performance, the correlation is weaker. As such, the maximum entropy method can be used to quantify the overall quality of a retrieval measure. Furthermore, for good measures of overall performance (such as average precision), we show that the corresponding maximum entropy distributions can be used to accurately infer precision-recall curves and the values of other measures of performance, and we demonstrate that the quality of these inferences far exceeds that predicted by simple retrieval measure correlation, as demonstrated through TREC data.

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Index Terms

The maximum entropy method for analyzing retrieval measures
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results

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cover image ACM Conferences

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

Copyright © 2005 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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Published: 15 August 2005

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SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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

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https://dl.acm.org/doi/10.1016/j.eswa.2023.122685
Khramtsova EZhuang SBaktashmotlagh MWang XZuccon G(2023)Selecting which Dense Retriever to use for Zero-Shot SearchProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625330(223-233)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625330
Feng DKarmaker S(2023)Joint upper & expected value normalization for evaluation of retrieval systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10340460:4Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103404
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