Ordinal random fields for recommender systems

Liu, S; Tran, T; Li, G; Jiang, Y

Ordinal random fields for recommender systems

Liu, S

Tran, T Li, G Jiang, Y

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Publication Type:: Conference Proceeding
Citation:: Journal of Machine Learning Research, 2014, 39 (2014), pp. 283 - 298
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Liu, S https://orcid.org/0000-0001-6062-6580	en_US
dc.contributor.author	Tran, T	en_US
dc.contributor.author	Li, G	en_US
dc.contributor.author	Jiang, Y	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Journal of Machine Learning Research, 2014, 39 (2014), pp. 283 - 298	en_US
dc.identifier.issn	1532-4435	en_US
dc.identifier.uri	http://hdl.handle.net/10453/104851
dc.description.abstract	© 2014 S. Liu, T. Tran, G. Li & Y. Jiang. Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.	en_US
dc.relation.ispartof	Journal of Machine Learning Research	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Ordinal random fields for recommender systems	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2014	en_US
utslib.citation.volume	39	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access
pubs.issue	2014	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

Abstract:

© 2014 S. Liu, T. Tran, G. Li & Y. Jiang. Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.

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http://hdl.handle.net/10453/104851