Collaborative filtering:  A machine learning perspective.
Published: 2004
Language: eng
Number of pages: 125
Description: 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.
Notes: Adviser:  Richard Zemel.

Thesis (M.Sc.)--University of Toronto, 2004.

Electronic version licensed for access by U. of T. users.

Source: Masters Abstracts International, Volume: 42-06, page: 2239.

MICR copy on microfiche.