Article

Gravitation-based model for information retrieval

Authors:

Wei-Ying MaAuthors Info & Claims

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

Pages 488 - 495

https://doi.org/10.1145/1076034.1076117

Published: 15 August 2005 Publication History

Abstract

This paper proposes GBM (gravitation-based model), a physical model for information retrieval inspired by Newton's theory of gravitation. A mapping is built in this model from concepts of information retrieval (documents, queries, relevance, etc) to those of physics (mass, distance, radius, attractive force, etc). This model actually provides a new perspective on IR problems. A family of effective term weighting functions can be derived from it, including the well-known BM25 formula. This model has some advantages over most existing ones: First, because it is directly based on basic physical laws, the derived formulas and algorithms can have their explicit physical interpretation. Second, the ranking formulas derived from this model satisfy more intuitive heuristics than most of existing ones, thus have the potential to behave empirically better and to be used safely on various settings. Finally, a new approach for structured document retrieval derived from this model is more reasonable and behaves better than existing ones.

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Cited By

Bondarenko AFröbe MReimer JStein BVölske MHagen MAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Axiomatic Retrieval Experimentation with ir_axiomsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531743(3131-3140)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531743
Câmara AHauff C(2020)Diagnosing BERT with Retrieval HeuristicsAdvances in Information Retrieval10.1007/978-3-030-45439-5_40(605-618)Online publication date: 8-Apr-2020
https://doi.org/10.1007/978-3-030-45439-5_40
Sanz-Cruzado JMacdonald COunis ICastells P(2020)Axiomatic Analysis of Contact Recommendation Methods in Social Networks: An IR PerspectiveAdvances in Information Retrieval10.1007/978-3-030-45439-5_12(175-190)Online publication date: 8-Apr-2020
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Index Terms

Gravitation-based model for information retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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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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Association for Computing Machinery

New York, NY, United States

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Published: 15 August 2005

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SIGIR

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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Cited By

Bondarenko AFröbe MReimer JStein BVölske MHagen MAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Axiomatic Retrieval Experimentation with ir_axiomsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531743(3131-3140)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531743
Câmara AHauff C(2020)Diagnosing BERT with Retrieval HeuristicsAdvances in Information Retrieval10.1007/978-3-030-45439-5_40(605-618)Online publication date: 8-Apr-2020
https://doi.org/10.1007/978-3-030-45439-5_40
Sanz-Cruzado JMacdonald COunis ICastells P(2020)Axiomatic Analysis of Contact Recommendation Methods in Social Networks: An IR PerspectiveAdvances in Information Retrieval10.1007/978-3-030-45439-5_12(175-190)Online publication date: 8-Apr-2020
https://doi.org/10.1007/978-3-030-45439-5_12
Ying ZHuang JZhou J(2019)A New Digital Signal Processing Based Model With Multi-Aspect Term Frequency for Information RetrievalIEEE Access10.1109/ACCESS.2019.29462887(160738-160754)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2946288
Ying ZHuang JZhou JJian FHe T(2019)DSPF: A Digital Signal Processing Based Framework for Information RetrievalIEEE Access10.1109/ACCESS.2019.29273297(110235-110248)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2927329
Ruixiang OYao HFeng PHui P(2019)Research on information retrieval model under scarcity theory and user cognitionComputers and Electrical Engineering10.1016/j.compeleceng.2019.04.00876:C(353-363)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1016/j.compeleceng.2019.04.008
Rennings DMoraes FHauff C(2019)An Axiomatic Approach to Diagnosing Neural IR ModelsAdvances in Information Retrieval10.1007/978-3-030-15712-8_32(489-503)Online publication date: 7-Apr-2019
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Costa ABuccio EMelucci M(2015)A Document Retrieval Model Based on Digital Signal FilteringACM Transactions on Information Systems10.1145/280978734:1(1-37)Online publication date: 22-Sep-2015
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Raman SVijay Kumar Chaurasiya Venkatesan S(2012)Performance comparison of various information retrieval models used in search engines2012 International Conference on Communication, Information & Computing Technology (ICCICT)10.1109/ICCICT.2012.6398124(1-4)Online publication date: Oct-2012
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Murugeshan MMukherjee S(2012)Novel Relevance Model for Sentiment Classification Based on Collision TheoryAdvances in Communication, Network, and Computing10.1007/978-3-642-35615-5_67(417-421)Online publication date: 2012
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