Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis
Abstract
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 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.
Cite
Text
Tran et al. "Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis." Proceedings of the Fourth Asian Conference on Machine Learning, 2012.Markdown
[Tran et al. "Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis." Proceedings of the Fourth Asian Conference on Machine Learning, 2012.](https://mlanthology.org/acml/2012/tran2012acml-cumulative/)BibTeX
@inproceedings{tran2012acml-cumulative,
title = {{Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis}},
author = {Tran, Truyen and Phung, Dinh and Venkatesh, Svetha},
booktitle = {Proceedings of the Fourth Asian Conference on Machine Learning},
year = {2012},
pages = {411-426},
volume = {25},
url = {https://mlanthology.org/acml/2012/tran2012acml-cumulative/}
}