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

TL;DR

This paper introduces an innovative Bayesian collaborative filtering method that employs Reversible Jump MCMC within simulated annealing to automatically determine feature dimensions, with hyper-parameter tuning to enhance convergence on MovieLens data.

Contribution

It presents a novel empirical Bayes approach combined with Reversible Jump MCMC for automatic feature dimension selection in recommendation systems.

Findings

Effective in selecting feature dimensions on MovieLens dataset

Hyper-parameter tuning improves convergence and initial parameter estimation

Method can be applied to datasets with slow MCMC convergence or oscillations.

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.