A latent usage approach for clustering web transaction and building user profile

Zhang, Y; Xu, G; Zhou, X

A latent usage approach for clustering web transaction and building user profile

Zhang, Y Xu, G

Zhou, X

Permalink

Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005, 3584 LNAI pp. 31 - 42
Issue Date:: 2005-12-01

Closed Access

	Filename	Description	Size
	10.1.1.130.5758.pdf	Published Version	149.49 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Zhou, X	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005, 3584 LNAI pp. 31 - 42	en_US
dc.identifier.isbn	354027894X	en_US
dc.identifier.isbn	9783540278948	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33766
dc.description.abstract	Web transaction data between web visitors and web functionalities usually convey users' task-oriented behavior patterns. Clustering web transactions, thus, may capture such informative knowledge, in turn, build user profiles, which are associated with different navigational patterns. For some advanced web applications, such as web recommendation or personalization, the aforementioned work is crucial to make web users get their preferred information accurately. On the other hand, the conventional web usage mining techniques for clustering web objects often perform clustering on usage data directly rather than take the underlying semantic relationships among the web objects into account. Latent Semantic Analysis (LSA) model is a commonly used approach for capturing semantic associations among co-occurrence observations.. In this paper, we propose a LSA-based approach for such purpose. We demonstrated usability and scalability of the proposed approach through performing experiments on two real world datasets. The experimental results have validated the method's effectiveness in comparison with some previous studies. © Springer-Verlag Berlin Heidelberg 2005.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A latent usage approach for clustering web transaction and building user profile	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3584 LNAI	en_US
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
pubs.volume	3584 LNAI	en_US

Abstract:

Web transaction data between web visitors and web functionalities usually convey users' task-oriented behavior patterns. Clustering web transactions, thus, may capture such informative knowledge, in turn, build user profiles, which are associated with different navigational patterns. For some advanced web applications, such as web recommendation or personalization, the aforementioned work is crucial to make web users get their preferred information accurately. On the other hand, the conventional web usage mining techniques for clustering web objects often perform clustering on usage data directly rather than take the underlying semantic relationships among the web objects into account. Latent Semantic Analysis (LSA) model is a commonly used approach for capturing semantic associations among co-occurrence observations.. In this paper, we propose a LSA-based approach for such purpose. We demonstrated usability and scalability of the proposed approach through performing experiments on two real world datasets. The experimental results have validated the method's effectiveness in comparison with some previous studies. © Springer-Verlag Berlin Heidelberg 2005.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/33766