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

Arabin Kumar Dey; Himanshu Jhamb

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arXiv · 1808.05480

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

Arabin Kumar Dey·Himanshu Jhamb

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Abstract

In this article we select the unknown dimension of the feature by re- versible jump MCMC inside a simulated annealing in bayesian set up of collaborative filter. We implement the same in MovieLens small dataset. We also tune the hyper parameter by using a modified empirical bayes. 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.

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BibTeX RIS

Arabin Kumar Dey, Himanshu Jhamb. 2018-08-15. A novel Empirical Bayes with Reversible Jump Markov Chain in User-Movie Recommendation system. https://arxiv.org/abs/1808.05480

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