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

IMP: A Message-Passing Algorithmfor Matrix Completion