Data-driven rank breaking for efficient rank aggregation

Ashish Khetan, Sewoong Oh

Research output: Contribution to journalArticlepeer-review


Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. Rank-breaking is a common practice to reduce the computational complexity of learning the global ranking. The individual preferences are broken into pairwise comparisons and applied to efficient algorithms tailored for independent paired comparisons. However, due to the ignored dependencies in the data, naive rank-breaking approaches can result in inconsistent estimates. The key idea to produce accurate and consistent estimates is to treat the pairwise comparisons unequally, depending on the topology of the collected data. In this paper, we provide the optimal rank-breaking estimator, which not only achieves consistency but also achieves the best error bound. This allows us to characterize the fundamental tradeo between accuracy and complexity. Further, the analysis identfies how the accuracy depends on the spectral gap of a corresponding comparison graph.

Original languageEnglish (US)
JournalJournal of Machine Learning Research
StatePublished - Oct 1 2016


  • Plackett-Luce model
  • Rank aggregation
  • Sample complexity

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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