  In recommender systems, users rate items, and are subsequently served other
product recommendations based on these ratings. Even though users usually rate
a tiny percentage of the available items, the system tries to estimate
unobserved preferences by finding similarities across users and across items.
In this work, we treat the observed ratings data as partially observed, dense,
weighted, bipartite networks. For a class of systems without outside
information, we adapt an approach developed for dense, weighted networks to
account for unobserved edges and the bipartite nature of the problem. This
approach allows for community structure, and for local estimation of flexible
patterns of ratings across different pairs of communities. We compare the
performance of our proposed approach to existing methods on a simulated data
set, as well as on a data set of joke ratings, examining model performance in
both cases at differing levels of sparsity.
