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Article

Modeling score distributions for combining the outputs of search engines

Authors:

F. FengAuthors Info & Claims

SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 267 - 275

https://doi.org/10.1145/383952.384005

Published: 01 September 2001 Publication History

Abstract

In this paper the score distributions of a number of text search engines are modeled. It is shown empirically that the score distributions on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents. Experiments show that this model fits TREC-3 and TREC-4 data for not only probabilistic search engines like INQUERY but also vector space search engines like SMART for English. We have also used this model to fit the output of other search engines like LSI search engines and search engines indexing other languages like Chinese.

It is then shown that given a query for which relevance information is not available, a mixture model consisting of an exponential and a normal distribution can be fitted to the score distribution. These distributions can be used to map the scores of a search engine to probabilities. We also discuss how the shape of the score distributions arise given certain assumptions about word distributions in documents. We hypothesize that all 'good' text search engines operating on any language have similar characteristics.

This model has many possible applications. For example, the outputs of different search engines can be combined by averaging the probabilities (optimal if the search engines are independent) or by using the probabilities to select the best engine for each query. Results show that the technique performs as well as the best current combination techniques.

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Cited By

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Bahri DZheng CTay YMetzler DTomkins AChen HDuh WHuang HKato MMothe JPoblete B(2023)Surprise: Result List Truncation via Extreme Value TheoryProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592066(2404-2408)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592066
Rizzo SBrucato MMontesi D(2022)Ranking Models for the Temporal Dimension of TextACM Transactions on Information Systems10.1145/356548141:2(1-34)Online publication date: 21-Dec-2022
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Index Terms

Modeling score distributions for combining the outputs of search engines
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
  2. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Information retrieval query processing
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

September 2001

454 pages

ISBN:1581133316

DOI:10.1145/383952

Chairmen:
Donald H. Kraft
Louisiana State Univ.
,
W. Bruce Croft
University of Massachusetts, (For the Americas)
,
David J. Harper
The Robert Gordon University, (For Europe and Africa)
,
Justin Zobel
RMIT University, (For Asia and Australasia)

Copyright © 2001 ACM.

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2001

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SIGIR01: 24th ACM/SIGIR International Conference on Research and Development in Information Retrieval

Louisiana, New Orleans, USA

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SIGIR '01 Paper Acceptance Rate 47 of 201 submissions, 23%;

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

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Cited By

Yang HGonçalves T(2024)Improving Consumer Health Search with Field-Level Learning-to-Rank TechniquesInformation10.3390/info1511069515:11(695)Online publication date: 3-Nov-2024
https://doi.org/10.3390/info15110695
Bahri DZheng CTay YMetzler DTomkins AChen HDuh WHuang HKato MMothe JPoblete B(2023)Surprise: Result List Truncation via Extreme Value TheoryProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592066(2404-2408)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592066
Rizzo SBrucato MMontesi D(2022)Ranking Models for the Temporal Dimension of TextACM Transactions on Information Systems10.1145/356548141:2(1-34)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1145/3565481
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
Wang DLi JZhu TZhou HZhu QWen YPiao HSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)MtCutProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498466(1054-1062)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498466
Wild SLast M(2022)Cost-Oriented Candidate Screening Using Machine Learning AlgorithmsRecent Challenges in Intelligent Information and Database Systems10.1007/978-981-19-8234-7_57(737-750)Online publication date: 24-Nov-2022
https://doi.org/10.1007/978-981-19-8234-7_57
Barman AShah S(2021)A Graph-Based Approach for Making Consensus-Based Decisions in Image Search and Person Re-IdentificationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2019.294459743:3(753-765)Online publication date: 1-Mar-2021
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Lillis D(2020)On the Evaluation of Data Fusion for Information RetrievalProceedings of the 12th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3441501.3441506(54-57)Online publication date: 16-Dec-2020
https://dl.acm.org/doi/10.1145/3441501.3441506
Bahri DTay YZheng CMetzler DTomkins AHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Choppy: Cut Transformer for Ranked List TruncationProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401188(1513-1516)Online publication date: 25-Jul-2020
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Yang HGonçalves T(2020)Aggregation on Learning to Rank for Consumer Health Information RetrievalModelling and Development of Intelligent Systems10.1007/978-3-030-39237-6_6(81-93)Online publication date: 17-Jan-2020
https://doi.org/10.1007/978-3-030-39237-6_6
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