One-and-only item recommendation with fuzzy logic techniques

Cornelis, C; Lu, J; Guo, X; Zhang, G

One-and-only item recommendation with fuzzy logic techniques

Cornelis, C Lu, J

Guo, X Zhang, G

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Publication Type:: Journal Article
Citation:: Information Sciences, 2007, 177 (22), pp. 4906 - 4921
Issue Date:: 2007-11-15

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Field	Value	Language
dc.contributor.author	Cornelis, C	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Guo, X	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2007-11-15	en_US
dc.identifier.citation	Information Sciences, 2007, 177 (22), pp. 4906 - 4921	en_US
dc.identifier.issn	0020-0255	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4147
dc.description.abstract	Recommender systems anticipate users' needs by suggesting items that are likely to interest them. Most existing systems employ collaborative filtering (CF) techniques, searching for regularities in the way users have rated items. While in general a successful approach, CF cannot cope well with so-called one-and-only items, that is: items of which there is only one single instance (like an event), and which as such cannot be repetitively "sold". Typically such items are evaluated only after they have ceased being available, thereby thwarting the classical CF strategy. In this paper, we develop a conceptual framework for recommending one-and-only items. It uses fuzzy logic, which allows to reflect the graded/uncertain information in the domain, and to extend the CF paradigm, overcoming limitations of existing techniques. A possible application in the context of trade exhibition recommendation for e-government is discussed to illustrate the proposed conceptual framework. © 2007 Elsevier Inc. All rights reserved.	en_US
dc.relation.ispartof	Information Sciences	en_US
dc.relation.isbasedon	10.1016/j.ins.2007.07.001	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	One-and-only item recommendation with fuzzy logic techniques	en_US
dc.type	Journal Article
utslib.citation.volume	22	en_US
utslib.citation.volume	177	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	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	22	en_US
pubs.publication-status	Published	en_US
pubs.volume	177	en_US

Abstract:

Recommender systems anticipate users' needs by suggesting items that are likely to interest them. Most existing systems employ collaborative filtering (CF) techniques, searching for regularities in the way users have rated items. While in general a successful approach, CF cannot cope well with so-called one-and-only items, that is: items of which there is only one single instance (like an event), and which as such cannot be repetitively "sold". Typically such items are evaluated only after they have ceased being available, thereby thwarting the classical CF strategy. In this paper, we develop a conceptual framework for recommending one-and-only items. It uses fuzzy logic, which allows to reflect the graded/uncertain information in the domain, and to extend the CF paradigm, overcoming limitations of existing techniques. A possible application in the context of trade exhibition recommendation for e-government is discussed to illustrate the proposed conceptual framework. © 2007 Elsevier Inc. All rights reserved.

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http://hdl.handle.net/10453/4147