Robust Bayesian Matrix Factorisation
Abstract
We analyse the noise arising in collaborative filtering when formalised as a probabilistic matrix factorisation problem. We show empirically that modelling row- and column-specific variances is important, the noise being in general non-Gaussian and heteroscedastic. We also advocate for the use of a Student-t prior for the latent features as the standard Gaussian is included as a special case. We derive several variational inference algorithms and estimate the hyperparameters by type-II maximum likelihood. Experiments on real data show that the predictive performance is significantly improved.
Cite
Text
Lakshminarayanan et al. "Robust Bayesian Matrix Factorisation." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[Lakshminarayanan et al. "Robust Bayesian Matrix Factorisation." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/lakshminarayanan2011aistats-robust/)BibTeX
@inproceedings{lakshminarayanan2011aistats-robust,
title = {{Robust Bayesian Matrix Factorisation}},
author = {Lakshminarayanan, Balaji and Bouchard, Guillaume and Archambeau, Cedric},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {425-433},
volume = {15},
url = {https://mlanthology.org/aistats/2011/lakshminarayanan2011aistats-robust/}
}