Article

Automatically combining ranking heuristics for HTML documents

Author:

Joaquin RapelaAuthors Info & Claims

WIDM '01: Proceedings of the 3rd international workshop on Web information and data management

Pages 61 - 67

https://doi.org/10.1145/502932.502945

Published: 09 November 2001 Publication History

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Abstract

Current search engines use several criteria or heuristics to rank HTML documents. HTML ranking heuristics need to be combined into a ranking function that given a text query returns a ranked list of HTML documents. The standard approach is to build a weighted average by manually estimating the importance of every heuristic and assigning a weight proportional to the estimated importance. In the current paper we apply an automatic method for combining HTML ranking heuristics. Using recall/precision evaluations we study the performance of the automatic method and using collections of HTML documents with different characteristics we show that the automatic method finds weights tailored to specific characteristics of each document collection

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Cited By

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Muntean CNardini FPerego RTonellotto NFrieder O(2020)Weighting Passages Enhances AccuracyACM Transactions on Information Systems10.1145/342868739:2(1-11)Online publication date: 17-Dec-2020
https://dl.acm.org/doi/10.1145/3428687
Desai MSpink A(2018)An algorithm to cluster documents based on relevanceInformation Processing and Management: an International Journal10.1016/j.ipm.2004.05.00341:5(1035-1049)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.ipm.2004.05.003
Trotman A(2018)Choosing document structure weightsInformation Processing and Management: an International Journal10.1016/j.ipm.2003.10.00341:2(243-264)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.ipm.2003.10.003
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Index Terms

Automatically combining ranking heuristics for HTML documents
1. Applied computing
  1. Document management and text processing
    1. Document preparation
      1. Markup languages
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
  2. World Wide Web
    1. Web data description languages
      1. Markup languages

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Published In

WIDM '01: Proceedings of the 3rd international workshop on Web information and data management

November 2001

87 pages

ISBN:1581134444

DOI:10.1145/502932

Conference Chair:
Ee-Peng Lim
Nanyang Technological University, Singapore

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: 09 November 2001

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CIKM01

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CIKM01: International Conference on Information and Knowledge Management

November 9 - 1, 2001

Georgia, Atlanta, USA

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Cited By

View all

Muntean CNardini FPerego RTonellotto NFrieder O(2020)Weighting Passages Enhances AccuracyACM Transactions on Information Systems10.1145/342868739:2(1-11)Online publication date: 17-Dec-2020
https://dl.acm.org/doi/10.1145/3428687
Desai MSpink A(2018)An algorithm to cluster documents based on relevanceInformation Processing and Management: an International Journal10.1016/j.ipm.2004.05.00341:5(1035-1049)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.ipm.2004.05.003
Trotman A(2018)Choosing document structure weightsInformation Processing and Management: an International Journal10.1016/j.ipm.2003.10.00341:2(243-264)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.ipm.2003.10.003
Beigbeder MGéry MLargeron C(2018)Using proximity and tag weights for focused retrieval in structured documentsKnowledge and Information Systems10.1007/s10115-014-0767-644:1(51-76)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1007/s10115-014-0767-6
Géry MLargeron C(2018)BM25t: a BM25 extension for focused information retrievalKnowledge and Information Systems10.1007/s10115-011-0426-032:1(217-241)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1007/s10115-011-0426-0
Géry MLargeron CThollard F(2009)UJM at INEX 2008Advances in Focused Retrieval10.1007/978-3-642-03761-0_5(46-53)Online publication date: 3-Sep-2009
https://dl.acm.org/doi/10.1007/978-3-642-03761-0_5
Gery MLargeron CThollard F(2008)Integrating Structure in the Probabilistic Model for Information RetrievalProceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 0110.1109/WIIAT.2008.346(763-769)Online publication date: 9-Dec-2008
https://dl.acm.org/doi/10.1109/WIIAT.2008.346

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