A case study of Empirical Bayes in Recommendation system

Presented at Department of Mathematics, IIT Guwahati, Indian Institute of Technology Guwahati, 2017

We provide a formulation of empirical bayes described by Atchadé (2011) to tune the hyperparameters of priors used in Bayesian set up of collaborative filter.

We implement the same in MovieLens small dataset. We see that it can be used to get a good initial choice for the parameters. It can also be used to guess an initial choice for hyper-parameters in grid search procedure even for the datasets where MCMC oscillates around the true value or takes long time to converge.

The Arxiv version of the work can be viewed here.

The e-print of the article is available here

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