  The bane of one-class collaborative filtering is interpreting and modelling
the latent signal from the missing class. In this paper we present a novel
Bayesian generative model for implicit collaborative filtering. It forms a core
component of the Xbox Live architecture, and unlike previous approaches,
delineates the odds of a user disliking an item from simply not considering it.
The latent signal is treated as an unobserved random graph connecting users
with items they might have encountered. We demonstrate how large-scale
distributed learning can be achieved through a combination of stochastic
gradient descent and mean field variational inference over random graph
samples. A fine-grained comparison is done against a state of the art baseline
on real world data.
