A New Similarity Measure-Based Collaborative Filtering Approach for Recommender Systems

Wang, W; Lu, J; Zhang, G

A New Similarity Measure-Based Collaborative Filtering Approach for Recommender Systems

Wang, W Lu, J

Zhang, G

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2014, 277 pp. 443 - 452
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Wang, W	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2014, 277 pp. 443 - 452	en_US
dc.identifier.isbn	9783642549236	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35647
dc.description.abstract	Collaborative filtering (CF) is the most popular recommendation approach in personalization techniques but still suffers from poor recommendation accuracy. This study incorporates fuzzy set technique and user-relevant analysis to improve the CF approach. It proposes an innovative fuzzy similarity measure (FSM) and user-relevant aggregation (URA) on recommendation approach. Experiments demonstrate that the FSM-URA approach significantly improves the prediction accuracy comparing to the existing recommendation approaches. © Springer-Verlag Berlin Heidelberg 2014.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-642-54924-3_42	en_US
dc.title	A New Similarity Measure-Based Collaborative Filtering Approach for Recommender Systems	en_US
dc.type	Conference Proceeding
utslib.citation.volume	277	en_US
utslib.for	080105 Expert Systems	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
dc.location.activity	Shenzhen,China
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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	277	en_US

Abstract:

Collaborative filtering (CF) is the most popular recommendation approach in personalization techniques but still suffers from poor recommendation accuracy. This study incorporates fuzzy set technique and user-relevant analysis to improve the CF approach. It proposes an innovative fuzzy similarity measure (FSM) and user-relevant aggregation (URA) on recommendation approach. Experiments demonstrate that the FSM-URA approach significantly improves the prediction accuracy comparing to the existing recommendation approaches. © Springer-Verlag Berlin Heidelberg 2014.

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http://hdl.handle.net/10453/35647