Variational Bayesian approach to movie rating prediction

SVD has found widespread use across a variety of machine learn- ing applications, where its output is interpreted as compact and informative representations of data. The Netflix Prize challenge, and collaborative filtering in general, is an ideal application for SVD, since the data is a matrix of ratings given by users to movies.

It is thus not surprising to ob- serve that most currently successful teams use SVD, either with an extension, or to interpolate with results returned by other algorithms. Unfortunately SVD can easily overfit due to the extreme data sparsity of the matrix in the Net- flix Prize challenge, and care must be taken to regularize properly. In this paper, we propose a Bayesian approach to alleviate overfitting in SVD, where priors are introduced and all pa- rameters are integrated out using variational inference.