Cumulative restricted Boltzmann machines for ordinal matrix data analysis
Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires,preferences etc. This paper investigates modelling of ordinal data with Gaussian restrictedBoltzmann machines (RBMs). In particular, we present the model architecture, learningand inference procedures for both ve...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
JMLR
2012
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| Subjects: | |
| Online Access: | http://jmlr.csail.mit.edu/proceedings/ http://hdl.handle.net/20.500.11937/4560 |
| Summary: | Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires,preferences etc. This paper investigates modelling of ordinal data with Gaussian restrictedBoltzmann machines (RBMs). In particular, we present the model architecture, learningand inference procedures for both vector-variate and matrix-variate ordinal data. We showthat our model is able to capture latent opinion prole of citizens around the world, andis competitive against state-of-art collaborative ltering techniques on large-scale publicdatasets. The model thus has the potential to extend application of RBMs to diversedomains such as recommendation systems, product reviews and expert assessments |
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