Coupled collaborative filtering for context-aware recommendation

Jiang, X; Liu, W; Cao, L; Long, G

Coupled collaborative filtering for context-aware recommendation

Jiang, X Liu, W

Cao, L

Long, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2015, 6 pp. 4172 - 4173
Issue Date:: 2015-06-01

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Field	Value	Language
dc.contributor.author	Jiang, X	en_US
dc.contributor.author	Liu, W https://orcid.org/0000-0002-3003-1313	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Long, G https://orcid.org/0000-0003-3740-9515	en_US
dc.date.issued	2015-06-01	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2015, 6 pp. 4172 - 4173	en_US
dc.identifier.isbn	9781577357049	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36264
dc.description.abstract	Context-aware features have been widely recognized as important factors in recommender systems. However, as a major technique in recommender systems, traditional Collaborative Filtering (CF) does not provide a straightforward way of integrating the context-aware information into personal recommendation. We propose a Coupled Collaborative Filtering (CCF) model to measure the contextual information and use it to improve recommendations. In the proposed approach, coupled similarity computation is designed to be calculated by inter-item, intra-context and inter-context interactions among item, user and context-ware factors. Experiments based on different types of CF models demonstrate the effectiveness of our design.	en_US
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.title	Coupled collaborative filtering for context-aware recommendation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Austin Texas, USA
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 - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

Context-aware features have been widely recognized as important factors in recommender systems. However, as a major technique in recommender systems, traditional Collaborative Filtering (CF) does not provide a straightforward way of integrating the context-aware information into personal recommendation. We propose a Coupled Collaborative Filtering (CCF) model to measure the contextual information and use it to improve recommendations. In the proposed approach, coupled similarity computation is designed to be calculated by inter-item, intra-context and inter-context interactions among item, user and context-ware factors. Experiments based on different types of CF models demonstrate the effectiveness of our design.

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http://hdl.handle.net/10453/36264