Magnitude-preserving ranking algorithms

Corinna Cortes, Mehryar Mohri, Ashish Rastogi

Research output: Contribution to conferencePaper

Abstract

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 key 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 compare these results with those obtained using an algorithm minimizing the pairwise misranking error and standard regression.

Original languageEnglish (US)
Pages169-176
Number of pages8
DOIs
StatePublished - Aug 23 2007
Event24th International Conference on Machine Learning, ICML 2007 - Corvalis, OR, United States
Duration: Jun 20 2007Jun 24 2007

Other

Other24th International Conference on Machine Learning, ICML 2007
CountryUnited States
CityCorvalis, OR
Period6/20/076/24/07

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ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Cortes, C., Mohri, M., & Rastogi, A. (2007). Magnitude-preserving ranking algorithms. 169-176. Paper presented at 24th International Conference on Machine Learning, ICML 2007, Corvalis, OR, United States. https://doi.org/10.1145/1273496.1273518