BizSeeker: A hybrid semantic recommendation system for personalized government-to-business e-services

Lu, J; Shambour, Q; Xu, Y; Lin, Q; Zhang, G

BizSeeker: A hybrid semantic recommendation system for personalized government-to-business e-services

Lu, J

Shambour, Q

Xu, Y Lin, Q Zhang, G

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Publication Type:: Journal Article
Citation:: Internet Research, 2010, 20 (3), pp. 342 - 365
Issue Date:: 2010-07-01

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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 https://orcid.org/0000-0002-3026-845X	en_US
dc.contributor.author	Xu, Y	en_US
dc.contributor.author	Lin, Q	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2010-07-01	en_US
dc.identifier.citation	Internet Research, 2010, 20 (3), pp. 342 - 365	en_US
dc.identifier.issn	1066-2243	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13499
dc.description.abstract	Purpose: The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e-services, in particular, business partner recommendation e-services for Australian small to medium enterprises (SMEs). Design/methodology/approach: The study first proposes a product semantic relevance model. It then develops a hybrid semantic recommendation approach which combines item-based collaborative filtering (CF) similarity and item-based semantic similarity techniques. This hybrid approach is implemented into an intelligent business-partner-locator recommendation-system prototype called BizSeeker. Findings: The hybrid semantic recommendation approach can help overcome the limitations of existing recommendation techniques. The recommendation system prototype, BizSeeker, can recommend relevant business partners to individual business users (e.g. exporters), which therefore will reduce the time, cost and risk of businesses involved in entering local and international markets. Practical implications: The study would be of great value in e-government personalization research. It would facilitate the transformation of the current G2B e-services into a new stage wherein the e-government agencies offer personalized e-services to business users. The study would help government policy decision-makers to increase the adoption of e-government services. Originality/value: Providing personalized e-services by e-government can be seen as an evolution of the intentions-based approach and will be one of the next directions of government e-services. This paper develops a new recommender approach and systems to improve personalization of government e-services. © Emerald Group Publishing Limited.	en_US
dc.relation.ispartof	Internet Research	en_US
dc.relation.isbasedon	10.1108/10662241011050740	en_US
dc.subject.classification	Marketing	en_US
dc.title	BizSeeker: A hybrid semantic recommendation system for personalized government-to-business e-services	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	20	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
dc.location.activity	ISI:000280441300007	en_US
dc.location.activity	Brisbane, Australia
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.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

Purpose: The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e-services, in particular, business partner recommendation e-services for Australian small to medium enterprises (SMEs). Design/methodology/approach: The study first proposes a product semantic relevance model. It then develops a hybrid semantic recommendation approach which combines item-based collaborative filtering (CF) similarity and item-based semantic similarity techniques. This hybrid approach is implemented into an intelligent business-partner-locator recommendation-system prototype called BizSeeker. Findings: The hybrid semantic recommendation approach can help overcome the limitations of existing recommendation techniques. The recommendation system prototype, BizSeeker, can recommend relevant business partners to individual business users (e.g. exporters), which therefore will reduce the time, cost and risk of businesses involved in entering local and international markets. Practical implications: The study would be of great value in e-government personalization research. It would facilitate the transformation of the current G2B e-services into a new stage wherein the e-government agencies offer personalized e-services to business users. The study would help government policy decision-makers to increase the adoption of e-government services. Originality/value: Providing personalized e-services by e-government can be seen as an evolution of the intentions-based approach and will be one of the next directions of government e-services. This paper develops a new recommender approach and systems to improve personalization of government e-services. © Emerald Group Publishing Limited.

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http://hdl.handle.net/10453/13499