A hybrid fuzzy-based personalized recommender system for telecom products/services

Zhang, Z; Lin, H; Liu, K; Wu, D; Zhang, G; Lu, J

A hybrid fuzzy-based personalized recommender system for telecom products/services

Zhang, Z Lin, H Liu, K Wu, D

Zhang, G

Lu, J

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Publication Type:: Journal Article
Citation:: Information Sciences, 2013, 235 pp. 117 - 129
Issue Date:: 2013-06-20

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Field	Value	Language
dc.contributor.author	Zhang, Z	en_US
dc.contributor.author	Lin, H	en_US
dc.contributor.author	Liu, K	en_US
dc.contributor.author	Wu, D https://orcid.org/0000-0002-2499-5159	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2013-06-20	en_US
dc.identifier.citation	Information Sciences, 2013, 235 pp. 117 - 129	en_US
dc.identifier.issn	0020-0255	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27511
dc.description.abstract	The Internet creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems are designed to automatically generate personalized suggestions of products/services to customers. Because various uncertainties exist within both product and customer data, it is a challenge to achieve high recommendation accuracy. This study develops a hybrid recommendation approach which combines user-based and item-based collaborative filtering techniques with fuzzy set techniques and applies it to mobile product and service recommendation. It particularly implements the proposed approach in an intelligent recommender system software called Fuzzy-based Telecom Product Recommender System (FTCP-RS). Experimental results demonstrate the effectiveness of the proposed approach and the initial application shows that the FTCP-RS can effectively help customers to select the most suitable mobile products or services. © 2012 Elsevier Inc. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP110103733
dc.relation.ispartof	Information Sciences	en_US
dc.relation.isbasedon	10.1016/j.ins.2013.01.025	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A hybrid fuzzy-based personalized recommender system for telecom products/services	en_US
dc.type	Journal Article
utslib.citation.volume	235	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	235	en_US

Abstract:

The Internet creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems are designed to automatically generate personalized suggestions of products/services to customers. Because various uncertainties exist within both product and customer data, it is a challenge to achieve high recommendation accuracy. This study develops a hybrid recommendation approach which combines user-based and item-based collaborative filtering techniques with fuzzy set techniques and applies it to mobile product and service recommendation. It particularly implements the proposed approach in an intelligent recommender system software called Fuzzy-based Telecom Product Recommender System (FTCP-RS). Experimental results demonstrate the effectiveness of the proposed approach and the initial application shows that the FTCP-RS can effectively help customers to select the most suitable mobile products or services. © 2012 Elsevier Inc. All rights reserved.

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http://hdl.handle.net/10453/27511