A case study of Empirical Bayes in User-Movie Recommendation system
This is an incremental improvement for researchers and practitioners in recommendation systems, offering a practical method to initialize hyperparameters more efficiently.
The authors applied an existing Empirical Bayes formulation to tune hyperparameters in a Bayesian collaborative filtering setup for a movie recommendation system, demonstrating its utility for obtaining good initial parameter choices and aiding grid search in cases where MCMC methods are slow or unstable.
In this article we provide a formulation of empirical bayes described by Atchade (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.