A web-based personalized business partner recommendation system using fuzzy semantic techniques

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

A web-based personalized business partner recommendation system using fuzzy semantic techniques

Lu, J

Shambour, Q

Xu, Y Lin, Q Zhang, G

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Publication Type:: Journal Article
Citation:: Computational Intelligence, 2013, 29 (1), pp. 37 - 69
Issue Date:: 2013-02-01

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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	2013-02-01	en_US
dc.identifier.citation	Computational Intelligence, 2013, 29 (1), pp. 37 - 69	en_US
dc.identifier.issn	0824-7935	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26302
dc.description.abstract	The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking "one-to-one" e-services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e-services. Recommender systems is an effective approach for the implementation of Personalized E-Service which has gained wide exposure in e-commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF-based recommendation approaches, namely sparsity and new "cold start" item problems. © 2012 Wiley Periodicals, Inc.	en_US
dc.relation.ispartof	Computational Intelligence	en_US
dc.relation.isbasedon	10.1111/j.1467-8640.2012.00427.x	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A web-based personalized business partner recommendation system using fuzzy semantic techniques	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	29	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0806 Information Systems	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	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking "one-to-one" e-services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e-services. Recommender systems is an effective approach for the implementation of Personalized E-Service which has gained wide exposure in e-commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF-based recommendation approaches, namely sparsity and new "cold start" item problems. © 2012 Wiley Periodicals, Inc.

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http://hdl.handle.net/10453/26302