Government-to-Business personalized e-services using semantic-enhanced recommender system

Shambour, Q; Lu, J

Government-to-Business personalized e-services using semantic-enhanced recommender system

Shambour, Q Lu, J

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 6866 LNCS pp. 197 - 211
Issue Date:: 2011-09-08

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Field	Value	Language
dc.contributor.author	Shambour, Q	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2011-09-08	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 6866 LNCS pp. 197 - 211	en_US
dc.identifier.isbn	9783642229602	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19180
dc.description.abstract	The information overload problem results in the under-use of some existing e-Government services. Recommender systems have proven to be an effective solution to the information overload problem by providing users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-Governments can support businesses, which are seeking 'one-to-one' e-services, on the problem of finding adequate business partners. For this purpose, a Hybrid Semantic-enhanced Collaborative Filtering (HSeCF) recommendation approach to provide personalized Government-to-Business (G2B) e-services, and in particular, business partner recommendation e-services for Small to Medium Businesses is proposed. Experimental results on two data sets, MovieLens and BizSeeker, show that the proposed HSeCF approach significantly outperforms the benchmark item-based CF algorithms, especially in dealing with sparsity or cold-start item problems. © 2011 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-22961-9_16	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Government-to-Business personalized e-services using semantic-enhanced recommender system	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6866 LNCS	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Toulouse, France	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	6866 LNCS	en_US

Abstract:

The information overload problem results in the under-use of some existing e-Government services. Recommender systems have proven to be an effective solution to the information overload problem by providing users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-Governments can support businesses, which are seeking 'one-to-one' e-services, on the problem of finding adequate business partners. For this purpose, a Hybrid Semantic-enhanced Collaborative Filtering (HSeCF) recommendation approach to provide personalized Government-to-Business (G2B) e-services, and in particular, business partner recommendation e-services for Small to Medium Businesses is proposed. Experimental results on two data sets, MovieLens and BizSeeker, show that the proposed HSeCF approach significantly outperforms the benchmark item-based CF algorithms, especially in dealing with sparsity or cold-start item problems. © 2011 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/19180