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Cumulative restricted Boltzmann machines for ordinal matrix data analysis
conference contribution
posted on 2012-01-01, 00:00 authored by Truyen TranTruyen Tran, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshOrdinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and matrix-variate ordinal data. We show that our model is able to capture latent opinion profile of citizens around the world, and is competitive against state-of-art collaborative filtering techniques on large-scale public datasets. The model thus has the potential to extend application of RBMs to diverse domains such as recommendation systems, product reviews and expert assessments.
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Event
Asian Conference on Machine Learning (4th : 2012 : Singapore)Pagination
411 - 426Publisher
JMLR : workshop and conference proceedingsLocation
SingaporePlace of publication
[Singapore]Start date
2012-11-04End date
2012-11-06Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2012, The AuthorsEditor/Contributor(s)
S Hoi, W BuntineTitle of proceedings
ACML 2012 : Proceedings of the 4th Asian Conference on Machine LearningUsage metrics
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