Optimization Of Collaborative Filtering For Large Data Sets

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,non-sequential collaborative filtering.We analyze existing methods for the task of rating prediction from a machine learning perspective.We show that the existing methods proposed for this task are simple applications or modifications of one or more standard machine learning methods for classification and regression.We introduce a new prediction method in these classes.We introduce a new experimental procedure for testing stronger forms of generalization than has been used previously.We conduct large-scale prediction accuracy experiments.We show interesting new results on the relative performance of these methods.