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The cluster hypothesis revisited

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SIGIR '85: Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 188 - 196

https://doi.org/10.1145/253495.253524

Published: 05 June 1985 Publication History

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Abstract

A new means of evaluating the cluster hypothesis is introduced and the results of such an evaluation are presented for four collections. The results of retrieval experiments comparing a sequential search, a cluster-based search, and a search of the clustered collection in which individual documents are scored against the query are also presented. These results indicate that while the absolute performance of a search on a particular collection is dependent on the pairwise similarity of the relevant documents, the relative effectiveness of clustered retrieval versus sequential retrieval is independent of this factor. However, retrieval of entire clusters in response to a query usually results in a poorer performance than retrieval of individual documents from clusters.

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Cited By

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Frayling EMacAvaney SMacdonald COunis I(2024)Effective Adhoc Retrieval Through Traversal of a Query-Document GraphAdvances in Information Retrieval10.1007/978-3-031-56063-7_6(89-104)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56063-7_6
Zhang HWang YChen QChang RZhang TMiao ZHou YDing YMiao XWang HPang BZhan YSun HDeng WZhang QYang FXie XYang MCui BOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Model-enhanced vector indexProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668518(54903-54917)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668518
MacAvaney STonellotto NMacdonald CAl Hasan MXiong L(2022)Adaptive Re-Ranking with a Corpus GraphProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557231(1491-1500)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557231
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The cluster hypothesis revisited
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Information systems
  1. Information retrieval

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Published In

SIGIR '85: Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval

June 1985

288 pages

ISBN:0897911598

DOI:10.1145/253495

Chairman:
Jean M. Tague
Univ. of Western Ontario, London, Ont., Canada

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: 05 June 1985

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Frayling EMacAvaney SMacdonald COunis I(2024)Effective Adhoc Retrieval Through Traversal of a Query-Document GraphAdvances in Information Retrieval10.1007/978-3-031-56063-7_6(89-104)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56063-7_6
Zhang HWang YChen QChang RZhang TMiao ZHou YDing YMiao XWang HPang BZhan YSun HDeng WZhang QYang FXie XYang MCui BOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Model-enhanced vector indexProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668518(54903-54917)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668518
MacAvaney STonellotto NMacdonald CAl Hasan MXiong L(2022)Adaptive Re-Ranking with a Corpus GraphProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557231(1491-1500)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557231
Kurland OTennenholtz MAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Competitive SearchProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532771(2838-2849)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532771
Markovskiy ERaiber FSabach SKurland OAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)From Cluster Ranking to Document RankingProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531819(2137-2141)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531819
Sarwar GO'Riordan C(2021)Passage Based Answer-Set Graph Approach for Query Performance PredictionProceedings of the 25th Australasian Document Computing Symposium10.1145/3503516.3503534(1-6)Online publication date: 9-Dec-2021
https://dl.acm.org/doi/10.1145/3503516.3503534
Sheetrit EFyodorov YRaiber FKurland OHasibi FFang YAizawa A(2021)Recommending Search Queries in Documents Using Inter N-Gram SimilaritiesProceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3471158.3472252(211-220)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3471158.3472252
Hashemi HZamani HCroft WDemartini GZuccon GCulpepper JHuang ZTong H(2021)Learning Multiple Intent Representations for Search QueriesProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482445(669-679)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482445
Hofstätter SLin SYang JLin JHanbury ADiaz FShah CSuel TCastells PJones RSakai T(2021)Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware SamplingProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462891(113-122)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462891
Sutanto TNayak R(2021)Fast Knowledge Discovery in Social Media Data using Clustering via Ranking2021 9th International Conference on Cyber and IT Service Management (CITSM)10.1109/CITSM52892.2021.9588866(1-8)Online publication date: 22-Sep-2021
https://doi.org/10.1109/CITSM52892.2021.9588866
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