Dimension Reduction for Supervised Ordering

Abstract

Ordered lists of objects are widely used as representational forms. Such ordered objects include Web search results and best-seller lists. Techniques for processing such ordinal data are being developed, particularly methods for the supervised ordering task: i.e., learning functions used to sort objects from sample orders. In this article, we propose dimension reduction methods specifically designed to improve prediction performance in supervised ordering tasks.