A hybrid recommendation approach for one-and-only items

Guo, X; Zhang, G; Chew, E; Burdon, S

A hybrid recommendation approach for one-and-only items

Guo, X Zhang, G

Chew, E

Burdon, S

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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), 2005, 3809 LNAI pp. 457 - 466
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Guo, X	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Chew, E https://orcid.org/0000-0002-5850-2455	en_US
dc.contributor.author	Burdon, S https://orcid.org/0000-0003-0073-8593	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005, 3809 LNAI pp. 457 - 466	en_US
dc.identifier.isbn	3540304622	en_US
dc.identifier.isbn	9783540304623	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1767
dc.description.abstract	Many mechanisms have been developed to deliver only relevant information to the web users and prevent information overload. The most popular recent developments in the e-commerce domain are the user-preference based personalization and recommendation techniques. However, the existing techniques have a major drawback - poor accuracy of recommendation on one-and-only items - because most of them do not understand the item's semantic features and attributes. Thus, in this study, we propose a novel Semantic Product Relevance model and its attendant personalized recommendation approach to assist Export business selecting the right international trade exhibitions for market promotion. A recommender system, called Smart Trade Exhibition Finder (STEF), is developed to tailor the relevant trade exhibition information to each particular business user. STEF reduces significantly the time, cost and risk faced by exporters in selecting, entering and developing international markets. In particular, the proposed model can be used to overcome the drawback of existing recommendation techniques. © Springer-Verlag Berlin Heidelberg 2005.	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.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A hybrid recommendation approach for one-and-only items	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3809 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, Australia	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 Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	3809 LNAI	en_US

Abstract:

Many mechanisms have been developed to deliver only relevant information to the web users and prevent information overload. The most popular recent developments in the e-commerce domain are the user-preference based personalization and recommendation techniques. However, the existing techniques have a major drawback - poor accuracy of recommendation on one-and-only items - because most of them do not understand the item's semantic features and attributes. Thus, in this study, we propose a novel Semantic Product Relevance model and its attendant personalized recommendation approach to assist Export business selecting the right international trade exhibitions for market promotion. A recommender system, called Smart Trade Exhibition Finder (STEF), is developed to tailor the relevant trade exhibition information to each particular business user. STEF reduces significantly the time, cost and risk faced by exporters in selecting, entering and developing international markets. In particular, the proposed model can be used to overcome the drawback of existing recommendation techniques. © Springer-Verlag Berlin Heidelberg 2005.

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http://hdl.handle.net/10453/1767