In this paper we develop a new Bayesian inference method for lowrank matrix reconstruction. We call the new method the RelevanceSingular Vector Machine (RSVM) where appropriate priors are definedon the singular vectors of the underlying matrix to promotelow rank. To accelerate computations, a numerically efficient approximationis developed. The proposed algorithms are applied tomatrix completion and matrix reconstruction problems and their performanceis studied numerically.