  Rating and recommendation systems have become a popular application area for
applying a suite of machine learning techniques. Current approaches rely
primarily on probabilistic interpretations and extensions of matrix
factorization, which factorizes a user-item ratings matrix into latent user and
item vectors. Most of these methods fail to model significant variations in
item ratings from otherwise similar users, a phenomenon known as the "Napoleon
Dynamite" effect. Recent efforts have addressed this problem by adding a
contextual bias term to the rating, which captures the mood under which a user
rates an item or the context in which an item is rated by a user. In this work,
we extend this model in a nonparametric sense by learning the optimal number of
moods or contexts from the data, and derive Gibbs sampling inference procedures
for our model. We evaluate our approach on the MovieLens 1M dataset, and show
significant improvements over the optimal parametric baseline, more than twice
the improvements previously encountered for this task. We also extract and
evaluate a DBLP dataset, wherein we predict the number of papers co-authored by
two authors, and present improvements over the parametric baseline on this
alternative domain as well.
