Modelling user behaviour for Web recommendation using LDA model

Xu, G; Zhang, Y; Yi, X

Modelling user behaviour for Web recommendation using LDA model

Xu, G

Zhang, Y Yi, X

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008, 2008, pp. 529 - 532
Issue Date:: 2008-12-01

Closed Access

	Filename	Description	Size
	2013002860OK.pdf	Published version	320.57 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	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Yi, X	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008, 2008, pp. 529 - 532	en_US
dc.identifier.isbn	9780769534961	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32048
dc.description.abstract	Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analysis of Web usage data will lead to discover Web user access pattern and facilitate users locate more preferable Web pages via collaborative recommending technique. Meanwhile, latent semantic analysis techniques provide a powerful means to capture user access pattern and associated task space. In this paper, we propose a collaborative Web recommendation framework, which employs Latent Dirichlet Allocation (LDA) to model underlying topic-simplex space and discover the associations between user sessions and multiple topics via probability inference. Experiments conducted on real Website usage dataset show that this approach can achieve better recommendation accuracy in comparison to existing techniques. The discovered topic-simplex expression can also provide a better interpretation of user navigational preference. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008	en_US
dc.relation.isbasedon	10.1109/WIIAT.2008.313	en_US
dc.title	Modelling user behaviour for Web recommendation using LDA model	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, NSW, Australia
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:

Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analysis of Web usage data will lead to discover Web user access pattern and facilitate users locate more preferable Web pages via collaborative recommending technique. Meanwhile, latent semantic analysis techniques provide a powerful means to capture user access pattern and associated task space. In this paper, we propose a collaborative Web recommendation framework, which employs Latent Dirichlet Allocation (LDA) to model underlying topic-simplex space and discover the associations between user sessions and multiple topics via probability inference. Experiments conducted on real Website usage dataset show that this approach can achieve better recommendation accuracy in comparison to existing techniques. The discovered topic-simplex expression can also provide a better interpretation of user navigational preference. © 2008 IEEE.

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

http://hdl.handle.net/10453/32048