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For large matrix factorisation problems, we develop a distributed MarkovChain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics(SGLD) that we call Parallel SGLD (PSGLD). PSGLD has very favourable scalingproperties with increasing data size and is comparable in terms ofcomputational requirements to optimisation methods based on stochastic gradientdescent. PSGLD achieves high performance by exploiting the conditionalindependence structure of the MF models to sub-sample data in a systematicmanner as to allow parallelisation and distributed computation. We provide aconvergence proof of the algorithm and verify its superior performance onvarious architectures such as Graphics Processing Units, shared memorymulti-core systems and multi-computer clusters.

Parallel Stochastic Gradient Markov Chain Monte Carlo for Matrix Factorisation Models