A trust-semantic fusion-based recommendation approach for e-business applications

Shambour, Q; Lu, J

A trust-semantic fusion-based recommendation approach for e-business applications

Shambour, Q

Lu, J

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Publication Type:: Journal Article
Citation:: Decision Support Systems, 2012, 54 (1), pp. 768 - 780
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Shambour, Q https://orcid.org/0000-0002-3026-845X	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Decision Support Systems, 2012, 54 (1), pp. 768 - 780	en_US
dc.identifier.issn	0167-9236	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22875
dc.description.abstract	Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsity, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative Trust-Semantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach. © 2012 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Decision Support Systems	en_US
dc.relation.isbasedon	10.1016/j.dss.2012.09.005	en_US
dc.subject.classification	Information Systems	en_US
dc.title	A trust-semantic fusion-based recommendation approach for e-business applications	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	54	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	54	en_US

Abstract:

Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsity, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative Trust-Semantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach. © 2012 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/22875