Online ranking by projecting

Koby Crammer, Yoram Singer

Research output: Contribution to journalArticle

28 Scopus citations

Abstract

We discuss the problem of ranking instances. In our framework, each instance is associated with a rank or a rating, which is an integer in 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank that is as close as possible to the instance's true rank. We discuss a group of closely related online algorithms, analyze their performance in the mistake-bound model, and prove their correctness. We describe two sets of experiments, with synthetic data and with the EachMovie data set for collaborative filtering. In the experiments we performed, our algorithms outperform online algorithms for regression and classification applied to ranking.

Original languageEnglish (US)
Pages (from-to)145-175
Number of pages31
JournalNeural computation
Volume17
Issue number1
DOIs
StatePublished - Jan 1 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience

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