Recommendation technique-based government-to-business personalized e-services

Lu, J; Shambour, Q; Zhang, G

Recommendation technique-based government-to-business personalized e-services

Lu, J

Shambour, Q Zhang, G

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Publication Type:: Conference Proceeding
Citation:: Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 2009
Issue Date:: 2009-11-02

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Field	Value	Language
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Shambour, Q	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2009-11-02	en_US
dc.identifier.citation	Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 2009	en_US
dc.identifier.isbn	9781424445776	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10845
dc.description.abstract	One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy sets-based semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners. ©2009 IEEE.	en_US
dc.relation.ispartof	Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS	en_US
dc.relation.isbasedon	10.1109/NAFIPS.2009.5156456	en_US
dc.title	Recommendation technique-based government-to-business personalized e-services	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080105 Expert Systems	en_US
dc.location.activity	Cincinnati, Ohio, USA	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

Abstract:

One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy sets-based semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners. ©2009 IEEE.

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http://hdl.handle.net/10453/10845