  In this paper, we propose novel gossip algorithms for the low-rank
decentralized matrix completion problem. The proposed approach is on the
Riemannian Grassmann manifold that allows local matrix completion by different
agents while achieving asymptotic consensus on the global low-rank factors. The
resulting approach is scalable and parallelizable. Our numerical experiments
show the good performance of the proposed algorithms on various benchmarks.
