On web communities mining and recommendation

Zhang, Y; Xu, G

On web communities mining and recommendation

Zhang, Y Xu, G

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Publication Type:: Journal Article
Citation:: Concurrency Computation Practice and Experience, 2009, 21 (5), pp. 561 - 582
Issue Date:: 2009-04-10

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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.date.issued	2009-04-10	en_US
dc.identifier.citation	Concurrency Computation Practice and Experience, 2009, 21 (5), pp. 561 - 582	en_US
dc.identifier.issn	1532-0626	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33762
dc.identifier.uri	http://hdl.handle.net/10453/28895
dc.description.abstract	Because of the lack of a uniform schema for web documents and the sheer amount and dynamics of web data, both the effectiveness and the efficiency of information management and retrieval of web data are often unsatisfactory when using conventional data management and searching techniques. To address this issue, we have adopted web mining and web community analysis approaches. On the basis of the analysis of web document contents, hyperlinks analysis, user access logs and semantic analysis, we have developed various approaches or algorithms to construct and analyze web communities, and to make recommendations. This paper will introduce and discuss several approaches on web community mining and recommendation. Copyright © 2009 John Wiley & Sons, Ltd.	en_US
dc.relation.ispartof	Concurrency Computation Practice and Experience	en_US
dc.relation.isbasedon	10.1002/cpe.1366	en_US
dc.subject.classification	Distributed Computing	en_US
dc.title	On web communities mining and recommendation	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	21	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0805 Distributed Computing	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.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

Because of the lack of a uniform schema for web documents and the sheer amount and dynamics of web data, both the effectiveness and the efficiency of information management and retrieval of web data are often unsatisfactory when using conventional data management and searching techniques. To address this issue, we have adopted web mining and web community analysis approaches. On the basis of the analysis of web document contents, hyperlinks analysis, user access logs and semantic analysis, we have developed various approaches or algorithms to construct and analyze web communities, and to make recommendations. This paper will introduce and discuss several approaches on web community mining and recommendation. Copyright © 2009 John Wiley & Sons, Ltd.

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

http://hdl.handle.net/10453/28895