Recommending trade exhibitions by integrating semantic information with collaborative filtering

Guo, X; Lu, J

Recommending trade exhibitions by integrating semantic information with collaborative filtering

Guo, X Lu, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005, 2005, 2005 pp. 747 - 750
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Guo, X	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Proceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005, 2005, 2005 pp. 747 - 750	en_US
dc.identifier.isbn	076952415X	en_US
dc.identifier.isbn	9780769524153	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2601
dc.description.abstract	Recommender systems have gained successfully applications for the past ten years, particular in E-commerce domain. However, existing recommendation approaches can not effectively deal with recommendation issue of one-and-only items occurred in government-to-business services, e.g. recommendation of trade exhibitions. Thus, in this study, we propose a novel approach by integrating semantic information with the traditional item-based collaborative filtering, and attempt to help the businesses choose the right trade exhibitions at the right time. The outcome of this study will have tremendous significance in overcoming the 'new item' problem of existing recommendation approaches. © 2005 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005	en_US
dc.relation.isbasedon	10.1109/WI.2005.126	en_US
dc.title	Recommending trade exhibitions by integrating semantic information with collaborative filtering	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2005	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Paris, France	en_US
dc.location.activity	Reading, England
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	2005	en_US

Abstract:

Recommender systems have gained successfully applications for the past ten years, particular in E-commerce domain. However, existing recommendation approaches can not effectively deal with recommendation issue of one-and-only items occurred in government-to-business services, e.g. recommendation of trade exhibitions. Thus, in this study, we propose a novel approach by integrating semantic information with the traditional item-based collaborative filtering, and attempt to help the businesses choose the right trade exhibitions at the right time. The outcome of this study will have tremendous significance in overcoming the 'new item' problem of existing recommendation approaches. © 2005 IEEE.

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http://hdl.handle.net/10453/2601