Collaborative filtering was initially proposed as a framework for
filtering information based on the preferences of users, and has
since been refined in many different ways. This thesis is a
comprehensive study of rating-based, pure, non-sequential
collaborative filtering. We analyze existing methods for the task
of rating prediction from a machine learning perspective. We show
that many existing methods proposed for this task are simple applications
or modifications of one or more standard machine learning methods
for classification, regression, clustering, dimensionality
reduction, and density estimation. We introduce new prediction
methods in all of these classes. We introduce a new experimental
procedure for testing stronger forms of generalization than has
been used previously. We implement a total of nine
prediction methods, and conduct large scale prediction accuracy
experiments. We show interesting new results on the relative
performance of these methods.