Learning to order things

William W. Cohen, Robert E. Schapire, Yoram Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

158 Scopus citations


There are many applications in which it is desirable to order rather than classify instances. Here wc consider the problem of learning how to order, given feedback in the form of prefcrencc judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a preference function, of the form PREF(u, v), which indicates whether it is advisable to rank u before v. New instances are then ordered so as to maximize agreements with the learned preference function. We show that the problem of finding the ordering that agrees best with a preference function is NP-complete, even under very restrictive assumptions. Nevertheless, we describe a simple greedy algorithm that is guaranteed to find a good approximation. We then discuss an on-line learning algorithm, based on the "Hedge" algorithm, for finding a good linear combination of ranking "experts." We use the ordering algorithm combined with the on-line learning algorithm to find a combination of "search experts/' each of which is a domain-specific query expansion strategy for a WWW search engine, and present experimental results that demonstrate the merits of our approach.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 10 - Proceedings of the 1997 Conference, NIPS 1997
PublisherNeural information processing systems foundation
Number of pages7
ISBN (Print)0262100762, 9780262100762
StatePublished - 1998
Event11th Annual Conference on Neural Information Processing Systems, NIPS 1997 - Denver, CO, United States
Duration: Dec 1 1997Dec 6 1997

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Other11th Annual Conference on Neural Information Processing Systems, NIPS 1997
Country/TerritoryUnited States
CityDenver, CO

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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