Collaborative rating allocation

Du, Y; Xu, C; Tao, D

Collaborative rating allocation

Du, Y

Xu, C Tao, D

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2017, 0 pp. 1617 - 1623
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Du, Y https://orcid.org/0000-0003-1932-6732	en_US
dc.contributor.author	Xu, C	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2017, 0 pp. 1617 - 1623	en_US
dc.identifier.isbn	9780999241103	en_US
dc.identifier.issn	1045-0823	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126336
dc.description.abstract	This paper studies the collaborative rating allocation problem, in which each user has limited ratings on all items. These users are termed "energy limited". Different from existing methods which treat each rating independently, we investigate the geometric properties of a user's rating vector, and design a matrix completion method on the simplex. In this method, a user's rating vector is estimated by the combination of user profiles as basis points on the simplex. Instead of using Euclidean metric, a non-linear pull-back distance measurement from the sphere is adopted since it can depict the geometric constraints on each user's rating vector. The resulting objective function is then efficiently optimized by a Riemannian conjugate gradient method on the simplex. Experiments on real-world data sets demonstrate our model's competitiveness versus other collaborative rating prediction methods.	en_US
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence	en_US
dc.title	Collaborative rating allocation	en_US
dc.type	Conference Proceeding
utslib.citation.volume	0	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/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	0	en_US

Abstract:

This paper studies the collaborative rating allocation problem, in which each user has limited ratings on all items. These users are termed "energy limited". Different from existing methods which treat each rating independently, we investigate the geometric properties of a user's rating vector, and design a matrix completion method on the simplex. In this method, a user's rating vector is estimated by the combination of user profiles as basis points on the simplex. Instead of using Euclidean metric, a non-linear pull-back distance measurement from the sphere is adopted since it can depict the geometric constraints on each user's rating vector. The resulting objective function is then efficiently optimized by a Riemannian conjugate gradient method on the simplex. Experiments on real-world data sets demonstrate our model's competitiveness versus other collaborative rating prediction methods.

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http://hdl.handle.net/10453/126336