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In this article we select the unknown dimension of the feature by re-versible jump MCMC inside a simulated annealing in bayesian set up ofcollaborative filter. We implement the same in MovieLens small dataset. We alsotune the hyper parameter by using a modified empirical bayes. It can also beused to guess an initial choice for hyper-parameters in grid search procedureeven for the datasets where MCMC oscillates around the true value or takes longtime to converge.

A novel Empirical Bayes with Reversible Jump Markov Chain in User-Movie Recommendation system