Latent factor analysis for low-dimensional implicit preference prediction

Zhou, Z; Xu, G; Zhu, X; Liu, S

Latent factor analysis for low-dimensional implicit preference prediction

Zhou, Z Xu, G

Zhu, X Liu, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of 4th International Conference on Behavioral, Economic, and Socio-Cultural Computing, BESC 2017, 2017, 2018-January pp. 1 - 2
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Zhou, Z	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Liu, S https://orcid.org/0000-0001-6062-6580	en_US
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	Proceedings of 4th International Conference on Behavioral, Economic, and Socio-Cultural Computing, BESC 2017, 2017, 2018-January pp. 1 - 2	en_US
dc.identifier.isbn	9781538623657	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122322
dc.description.abstract	© 2017 IEEE. User preference prediction aims to predict a users future preferences on a large number of items according to his/her preference history. To achieve this goal, many models have been proposed, but mainly for explicit preference data, such as 5-star ratings. Nevertheless, real-world data are often in implicit format, such as purchase action, and the number of items is not always large. In this paper, we demonstrate the use of latent factor models for solving the task of predicting user preferences on implicit and low-dimensional dataset.	en_US
dc.relation.ispartof	Proceedings of 4th International Conference on Behavioral, Economic, and Socio-Cultural Computing, BESC 2017	en_US
dc.relation.isbasedon	10.1109/BESC.2017.8256380	en_US
dc.title	Latent factor analysis for low-dimensional implicit preference prediction	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
utslib.for	0806 Information Systems	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. User preference prediction aims to predict a users future preferences on a large number of items according to his/her preference history. To achieve this goal, many models have been proposed, but mainly for explicit preference data, such as 5-star ratings. Nevertheless, real-world data are often in implicit format, such as purchase action, and the number of items is not always large. In this paper, we demonstrate the use of latent factor models for solving the task of predicting user preferences on implicit and low-dimensional dataset.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/122317