User and document group approach of clustering in tagging systems

Pan, R; Xu, G; Dolog, P

User and document group approach of clustering in tagging systems

Pan, R Xu, G

Dolog, P

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Publication Type:: Conference Proceeding
Citation:: LWA 2010 - Lernen, Wissen und Adaptivitat - Learning, Knowledge, and Adaptivity, Workshop Proceedings, 2010, pp. 315 - 321
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Pan, R	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Dolog, P	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	LWA 2010 - Lernen, Wissen und Adaptivitat - Learning, Knowledge, and Adaptivity, Workshop Proceedings, 2010, pp. 315 - 321	en_US
dc.identifier.uri	http://hdl.handle.net/10453/107123
dc.description.abstract	In this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group.	en_US
dc.relation.ispartof	LWA 2010 - Lernen, Wissen und Adaptivitat - Learning, Knowledge, and Adaptivity, Workshop Proceedings	en_US
dc.title	User and document group approach of clustering in tagging systems	en_US
dc.type	Conference Proceeding
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	open_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we propose a spectral clustering approach for users and documents group modeling in order to capture the common preference and relatedness of users and documents, and to reduce the time complexity of similarity calculations. In experiments, we investigate the selection of the optimal amount of clusters. We also show a reduction of the time consuming in calculating the similarity for the recommender systems by selecting a centroid first, and then compare the inside item on behalf of each group.

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http://hdl.handle.net/10453/107123