  We present a novel, scalable and Bayesian approach to modelling the
occurrence of pairs of symbols (i,j) drawn from a large vocabulary. Observed
pairs are assumed to be generated by a simple popularity based selection
process followed by censoring using a preference function. By basing inference
on the well-founded principle of variational bounding, and using new
site-independent bounds, we show how a scalable inference procedure can be
obtained for large data sets. State of the art results are presented on
real-world movie viewing data.
