The design and implementation of composite Collaborative Filtering algorithm for personalized recommendation

Hu, L; Wang, W; Wang, F; Zhang, X; Zhao, K

The design and implementation of composite Collaborative Filtering algorithm for personalized recommendation

Hu, L Wang, W Wang, F Zhang, X Zhao, K

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Publication Type:: Journal Article
Citation:: Journal of Software, 2012, 7 (9), pp. 2040 - 2045
Issue Date:: 2012-10-18

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Field	Value	Language
dc.contributor.author	Hu, L	en_US
dc.contributor.author	Wang, W	en_US
dc.contributor.author	Wang, F	en_US
dc.contributor.author	Zhang, X	en_US
dc.contributor.author	Zhao, K	en_US
dc.date.issued	2012-10-18	en_US
dc.identifier.citation	Journal of Software, 2012, 7 (9), pp. 2040 - 2045	en_US
dc.identifier.issn	1796-217X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114743
dc.description.abstract	A composite collaborative filtering algorithm for personalized recommend will be presented to solve the original Collaborative Filtering algorithm problem including 'None of User Starting' and 'Data Sparsity', and the Spearman rank correlation coefficient will be used as a main correlation coefficient. Top-M commended is going to be used to get the final results in this paper. At last, we will validate that this algorithm is superior to the algorithm of collaborative filtering based on user and the algorithm of collaborative filtering based on item. © 2012 ACADEMY PUBLISHER.	en_US
dc.relation.ispartof	Journal of Software	en_US
dc.relation.isbasedon	10.4304/jsw.7.9.2040-2045	en_US
dc.title	The design and implementation of composite Collaborative Filtering algorithm for personalized recommendation	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	7	en_US
utslib.for	0803 Computer Software	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/Students
utslib.copyright.status	open_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

A composite collaborative filtering algorithm for personalized recommend will be presented to solve the original Collaborative Filtering algorithm problem including 'None of User Starting' and 'Data Sparsity', and the Spearman rank correlation coefficient will be used as a main correlation coefficient. Top-M commended is going to be used to get the final results in this paper. At last, we will validate that this algorithm is superior to the algorithm of collaborative filtering based on user and the algorithm of collaborative filtering based on item. © 2012 ACADEMY PUBLISHER.

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http://hdl.handle.net/10453/114743