A novel Empirical Bayes with Reversible Jump Markov Chain in User-Movie Recommendation system
This work addresses hyperparameter tuning and feature dimension selection in recommendation systems, but it appears incremental as it builds on existing Bayesian and MCMC techniques without introducing a fundamentally new approach.
The authors tackled the problem of selecting unknown feature dimensions in collaborative filtering by using reversible jump MCMC within a simulated annealing Bayesian setup, and they tuned hyperparameters with a modified empirical Bayes method, implementing it on the MovieLens small dataset.
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.