  Collaborative filtering is an effective recommendation technique wherein the
preference of an individual can potentially be predicted based on preferences
of other members. Early algorithms often relied on the strong locality in the
preference data, that is, it is enough to predict preference of a user on a
particular item based on a small subset of other users with similar tastes or
of other items with similar properties. More recently, dimensionality reduction
techniques have proved to be equally competitive, and these are based on the
co-occurrence patterns rather than locality. This paper explores and extends a
probabilistic model known as Boltzmann Machine for collaborative filtering
tasks. It seamlessly integrates both the similarity and co-occurrence in a
principled manner. In particular, we study parameterisation options to deal
with the ordinal nature of the preferences, and propose a joint modelling of
both the user-based and item-based processes. Experiments on moderate and
large-scale movie recommendation show that our framework rivals existing
well-known methods.
