  A new message-passing (MP) method is considered for the matrix completion
problem associated with recommender systems. We attack the problem using a
(generative) factor graph model that is related to a probabilistic low-rank
matrix factorization. Based on the model, we propose a new algorithm, termed
IMP, for the recovery of a data matrix from incomplete observations. The
algorithm is based on a clustering followed by inference via MP (IMP). The
algorithm is compared with a number of other matrix completion algorithms on
real collaborative filtering (e.g., Netflix) data matrices. Our results show
that, while many methods perform similarly with a large number of revealed
entries, the IMP algorithm outperforms all others when the fraction of observed
entries is small. This is helpful because it reduces the well-known cold-start
problem associated with collaborative filtering (CF) systems in practice.
