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Centre for Pattern Recognition and Data Analytics
School of Information Technology
Locked Bag 20000
GEELONG VIC 3220
T. Tran, D.Q. Phung, and S. Venkatesh. Cumulative restricted Boltzmann machines for ordinal matrix data analysis. In Proceedings of 4th Asian Conference on Machine Learning, 2012.
Ordinal 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 prole of citizens around the world, and is competitive against state-of-art collaborative ltering 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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27th February 2015