  We are interested in learning customers' video preferences from their
historic viewing patterns and geographical location. We consider a Bayesian
latent factor modeling approach for this task. In order to tune the complexity
of the model to best represent the data, we make use of Bayesian nonparameteric
techniques. We describe an inference technique that can scale to large
real-world data sets. Finally we show results obtained by applying the model to
a large internal Netflix data set, that illustrates that the model was able to
capture interesting relationships between viewing patterns and geographical
location.
