Pranking with ranking

Koby Crammer, Yoram Singer

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

214 Scopus citations


We discuss the problem of ranking instances. In our framework each instance is associated with a rank or a rating, which is an integer from 1 to k. Our goal is t o find a rank-prediction rule t hat assigns each instance a rank which is as close as possible to the instance's true rank. We describe a simple and efficient online algorithm, analyze its performance in the mistake bound model, and prove its correctness. We describe two sets of experiments, with synthetic data and with the EachMovie dataset. for collaborative filtering. In the experiments we performed, our algorithm outperforms online algorit hms for regression and classification applied t o ranking.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001
PublisherNeural information processing systems foundation
ISBN (Print)0262042088, 9780262042086
StatePublished - 2002
Event15th Annual Neural Information Processing Systems Conference, NIPS 2001 - Vancouver, BC, Canada
Duration: Dec 3 2001Dec 8 2001

Publication series

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


Other15th Annual Neural Information Processing Systems Conference, NIPS 2001
CityVancouver, BC

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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