article

Web site personalization based on link analysis and navigational patterns

Authors:

Magdalini Eirinaki,

Michalis VazirgiannisAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 7, Issue 4

Pages 21 - es

https://doi.org/10.1145/1278366.1278370

Published: 01 October 2007 Publication History

Abstract

The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of online information services. The need for predicting the users' needs in order to improve the usability and user retention of a Web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing “next” pages to users based on their current visit and past users' navigational patterns. In the vast majority of related algorithms, however, only the usage data is used to produce recommendations, disregarding the structural properties of the Web graph. Thus important—in terms of PageRank authority score—pages may be underrated. In this work, we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to Web pages based on their importance in the Web site's navigational graph. We propose the application of a localized version of UPR (l-UPR) to personalized navigational subgraphs for online Web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.

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Cited By

Uddin MRizvi SRazaque A(2018)Proposing Logical Table Constructs for Enhanced Machine Learning ProcessIEEE Access10.1109/ACCESS.2018.28660466(47751-47769)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2866046
Lin TWu ZChen Y(2015)Using High-Frequency Interaction Events to Automatically Classify Cognitive LoadHuman Behavior, Psychology, and Social Interaction in the Digital Era10.4018/978-1-4666-8450-8.ch010(210-228)Online publication date: 2015
https://doi.org/10.4018/978-1-4666-8450-8.ch010
Forsati RMoayedikia AShamsfard M(2015)An effective Web page recommender using binary data clusteringInformation Retrieval10.1007/s10791-015-9252-418:3(167-214)Online publication date: 1-Jun-2015
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Index Terms

Web site personalization based on link analysis and navigational patterns
1. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Web applications
    2. Web services

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cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 7, Issue 4

October 2007

153 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/1278366

Issue’s Table of Contents

Copyright © 2007 ACM.

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Publication History

Published: 01 October 2007

Published in TOIT Volume 7, Issue 4

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Cited By

Uddin MRizvi SRazaque A(2018)Proposing Logical Table Constructs for Enhanced Machine Learning ProcessIEEE Access10.1109/ACCESS.2018.28660466(47751-47769)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2866046
Lin TWu ZChen Y(2015)Using High-Frequency Interaction Events to Automatically Classify Cognitive LoadHuman Behavior, Psychology, and Social Interaction in the Digital Era10.4018/978-1-4666-8450-8.ch010(210-228)Online publication date: 2015
https://doi.org/10.4018/978-1-4666-8450-8.ch010
Forsati RMoayedikia AShamsfard M(2015)An effective Web page recommender using binary data clusteringInformation Retrieval10.1007/s10791-015-9252-418:3(167-214)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s10791-015-9252-4
Padmaja KNageswara Rao KMurthy J(2014)Defending Approach against Forceful Browsing in Web ApplicationsICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II10.1007/978-3-319-03095-1_71(651-659)Online publication date: 2014
https://doi.org/10.1007/978-3-319-03095-1_71
Luis Ortega JAguillo I(2013)Network visualisation as a way to the web usage analysisAslib Proceedings10.1108/0001253131129717765:1(40-53)Online publication date: Jan-2013
https://doi.org/10.1108/00012531311297177
Khonsha SSadreddini M(2011)New hybrid web personalization framework2011 IEEE 3rd International Conference on Communication Software and Networks10.1109/ICCSN.2011.6014395(86-92)Online publication date: May-2011
https://doi.org/10.1109/ICCSN.2011.6014395
Mehr STaran MHashemi AMeybodi M(2011)A new recommendation algorithm using distributed learning automata and graph partitioning2011 11th International Conference on Hybrid Intelligent Systems (HIS)10.1109/HIS.2011.6122131(351-357)Online publication date: Dec-2011
https://doi.org/10.1109/HIS.2011.6122131
Sakkopoulos EAntoniou DAdamopoulou PTsirakis NTsakalidis A(2010)A web personalizing technique using adaptive data structuresJournal of Systems and Software10.1016/j.jss.2010.06.02683:11(2200-2210)Online publication date: 1-Nov-2010
https://dl.acm.org/doi/10.1016/j.jss.2010.06.026
Borges JLevene M(2009)Variable Length Markov Chains for Web Usage MiningEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch310(2031-2035)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-010-3.ch310
Rosaci DSarné GGarruzzo S(2009)MUADDIBACM Transactions on Information Systems10.1145/1629096.162910227:4(1-41)Online publication date: 30-Nov-2009
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