Unsupervised rank aggregation with distance-based models

Alexandre Klementiev, Dan Roth, Kevin Small

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


The need to meaningfully combine sets of rankings often comes up when one deals with ranked data. Although a number of heuristic and supervised learning approaches to rank aggregation exist, they require domain knowledge or supervised ranked data, both of which are expensive to acquire. In order to address these limitations, we propose a mathematical and algorithmic framework for learning to aggregate (partial) rankings without supervision. We instantiate the framework for the cases of combining permutations and combining top-k lists, and propose a novel metric for the latter. Experiments in both scenarios demonstrate the effectiveness of the proposed formalism.

Original languageEnglish (US)
Title of host publicationProceedings of the 25th International Conference on Machine Learning
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)9781605582054
StatePublished - 2008
Event25th International Conference on Machine Learning - Helsinki, Finland
Duration: Jul 5 2008Jul 9 2008

Publication series

NameProceedings of the 25th International Conference on Machine Learning


Other25th International Conference on Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software


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