Title: Magnitude-Preserving Ranking Algorithms 


Authors: Corinna Cortes, Mehryar Mohri, and Ashish Rastogi

This paper studies the learning problem of ranking when one wishes  not just to accurately predict
pairwise ordering but also preserve the magnitude of the preferences  or the difference between ratings,
a problem motivated by its crucial importance in the design of search  engines, movie recommendation,
and other similar ranking systems. We describe and analyze several  algorithms for this problem and
give stability bounds for their generalization error, extending  previously known stability results to non-
bipartite ranking and magnitude of preference-preserving algorithms.  We also report the results of
experiments comparing these algorithms on several datasets and  contrast these results with those obtained
using an AUC-maximization algorithm.