Article

A web personalization system based on web usage mining techniques

Authors:

Massimiliano Albanese,

Antonio Picariello,

Carlo Sansone,

Lucio SansoneAuthors Info & Claims

WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters

Pages 288 - 289

https://doi.org/10.1145/1013367.1013439

Published: 19 May 2004 Publication History

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Abstract

In the past few years, web usage mining techniques have grown rapidly together with the explosive growth of the web, both in the research and commercial areas. In this work we present a Web mining strategy for Web personalization based on a novel pattern recognition strategy which analyzes and classifies both static and dynamic features. The results of experiments on the data from a large commercial web site are presented to show the effectiveness of the proposed system.

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M. Eirinaki and M. Vazirgiannis. Web mining for web personalization. ACM TOIT., 3(1):2--27, Feb 2003.

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M. Mulvenna, S. Anand, and A. Buchner. Personalization on the net using web mining. CACM, 43(8):123--125, Aug. 2000.

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F. Zhang and H. Chang. Research and development in web usage mining system--key issues and proposed solutions: a survey. In First IEEE Int. Conf. on Machine Learning and Cybernetics Proceedings, pages 986--990, Nov. 2002.

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Cited By

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(2019)OUP accepted manuscriptThe Computer Journal10.1093/comjnl/bxz132Online publication date: 2019
https://doi.org/10.1093/comjnl/bxz132
Picariello ASansone C(2018)A web usage mining algorithm for web personalizationIntelligent Decision Technologies10.5555/1515884.15158872:4(219-230)Online publication date: 14-Dec-2018
https://dl.acm.org/doi/10.5555/1515884.1515887
Duwairi RAmmari H(2016)An enhanced CBAR algorithm for improving recommendation systems accuracySimulation Modelling Practice and Theory10.1016/j.simpat.2015.10.00160(54-68)Online publication date: Jan-2016
https://doi.org/10.1016/j.simpat.2015.10.001
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Index Terms

A web personalization system based on web usage mining techniques
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters

May 2004

532 pages

ISBN:1581139128

DOI:10.1145/1013367

Conference Chairs:
Stuart Feldman
IBM Research
,
Mike Uretsky
New York University
,
Program Chairs:
Marc Najork
Microsoft Research
,
Craig Wills
Worcester Polytechnic Institute

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: 19 May 2004

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WWW04

Sponsor:

WWW04: The 2004 World Wide Web Conference ( in conjunction with ACM Conference on Electronic Commerce [EC'04])

May 19 - 21, 2004

NY, New York, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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(2019)OUP accepted manuscriptThe Computer Journal10.1093/comjnl/bxz132Online publication date: 2019
https://doi.org/10.1093/comjnl/bxz132
Picariello ASansone C(2018)A web usage mining algorithm for web personalizationIntelligent Decision Technologies10.5555/1515884.15158872:4(219-230)Online publication date: 14-Dec-2018
https://dl.acm.org/doi/10.5555/1515884.1515887
Duwairi RAmmari H(2016)An enhanced CBAR algorithm for improving recommendation systems accuracySimulation Modelling Practice and Theory10.1016/j.simpat.2015.10.00160(54-68)Online publication date: Jan-2016
https://doi.org/10.1016/j.simpat.2015.10.001
Omari A(2013)Supporting Companies Management and Improving their Productivity through Mining Customers TransactionsData Mining10.4018/978-1-4666-2455-9.ch079(1519-1533)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-2455-9.ch079
Omari A(2011)Supporting Companies Management and Improving their Productivity through Mining Customers TransactionsE-Strategies for Resource Management Systems10.4018/978-1-61692-016-6.ch021(376-390)Online publication date: 2011
https://doi.org/10.4018/978-1-61692-016-6.ch021
Moh TSaxena N(2010)Personalizing Web Recommendations Using Web Usage Mining and Web Semantics with Time AttributeInformation Systems, Technology and Management10.1007/978-3-642-12035-0_24(244-254)Online publication date: 2010
https://doi.org/10.1007/978-3-642-12035-0_24
Lee HPark DHong MEkstrom JStockman M(2008)An instant messenger system for learner analysis in e-learning environmentProceedings of the 9th ACM SIGITE conference on Information technology education10.1145/1414558.1414572(51-52)Online publication date: 16-Oct-2008
https://dl.acm.org/doi/10.1145/1414558.1414572
Eirinaki MMavroeidis DTsatsaronis GVazirgiannis M(2005)Introducing semantics in web personalizationProceedings of the 2005 joint international conference on Semantics, Web and Mining10.1007/11908678_10(147-162)Online publication date: 3-Oct-2005
https://dl.acm.org/doi/10.1007/11908678_10

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Web mining for web personalization

Web usage mining: discovery and applications of usage patterns from Web data

Building Web Personalization System with Time-Driven Web Usage Mining