Application of Dimensionality Reduction in Recommender System

This paper presents two different experiments where
one technology called Singular Value Decomposition (SVD)
is explored to reduce the
dimensionality of recommender system databases.
Each experiment compares the quality of a
recommender system using SVD with the quality of a
recommender system using collaborative filtering.
The first experiment compares the effectiveness of
the two recommender systems at predicting consumer
preferences based on a database of explicit ratings of
products. The second experiment compares the
effectiveness of the two recommender systems at
producing Top-N lists based on a real-life customer
purchase database from an E-Commerce site.

Please see the link:
http://www.grouplens.org/papers/pdf/webKDD00.pdf