Using probabilistic latent semantic analysis for web page grouping

Xu, G; Zhang, Y; Zhou, X

Using probabilistic latent semantic analysis for web page grouping

Xu, G

Zhang, Y Zhou, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE International Workshop on Research Issues in Data Engineering, 2005, pp. 29 - 36
Issue Date:: 2005-10-31

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Field	Value	Language
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Zhou, X	en_US
dc.date.issued	2005-10-31	en_US
dc.identifier.citation	Proceedings of the IEEE International Workshop on Research Issues in Data Engineering, 2005, pp. 29 - 36	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33802
dc.description.abstract	The locality of web pages within a web site is initially determined by the designer's expectation. Web usage mining can discover the patterns in the navigational behaviour of web visitors, in turn, improve web site functionality and service designing by considering users' actual opinion. Conventional web page clustering technique is often utilized to reveal the functional similarity of web pages. However, high-dimensional computation problem will be incurred due to taking user transaction as dimension. In this paper, we propose a new web page grouping approach based on Probabilistic Latent Semantic Analysis (PLSA) model. An iterative algorithm based on maximum likelihood principle is employed to overcome the aforementioned computational shortcoming. The web pages are classified into various groups according to user access patterns. Meanwhile, the semantic latent factors or tasks are characterized by extracting the content of "dominant" pages related to the factors. We demonstrate the effectiveness of our approach by conducting experiments on real world data sets. © 2005 IEEE.	en_US
dc.relation.ispartof	Proceedings of the IEEE International Workshop on Research Issues in Data Engineering	en_US
dc.title	Using probabilistic latent semantic analysis for web page grouping	en_US
dc.type	Conference Proceeding
dc.location.activity	Tokyo, Japan
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

The locality of web pages within a web site is initially determined by the designer's expectation. Web usage mining can discover the patterns in the navigational behaviour of web visitors, in turn, improve web site functionality and service designing by considering users' actual opinion. Conventional web page clustering technique is often utilized to reveal the functional similarity of web pages. However, high-dimensional computation problem will be incurred due to taking user transaction as dimension. In this paper, we propose a new web page grouping approach based on Probabilistic Latent Semantic Analysis (PLSA) model. An iterative algorithm based on maximum likelihood principle is employed to overcome the aforementioned computational shortcoming. The web pages are classified into various groups according to user access patterns. Meanwhile, the semantic latent factors or tasks are characterized by extracting the content of "dominant" pages related to the factors. We demonstrate the effectiveness of our approach by conducting experiments on real world data sets. © 2005 IEEE.

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http://hdl.handle.net/10453/33802