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Reciprocal rank fusion outperforms condorcet and individual rank learning methods

Published:19 July 2009Publication History

ABSTRACT

Reciprocal Rank Fusion (RRF), a simple method for combining the document rankings from multiple IR systems, consistently yields better results than any individual system, and better results than the standard method Condorcet Fuse. This result is demonstrated by using RRF to combine the results of several TREC experiments, and to build a meta-learner that ranks the LETOR 3 dataset better than any previously reported method

References

  1. Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., and Li, H. Learning to rank: from pairwise approach to listwise approach. In ICML ’07 (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Freund, Y., Iyer, R., Schapire, R. E., and Singer, Y. An efficient boosting algorithm for combining preferences. JMLR 4 (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Joachims, T. Optimizing search engines using clickthrough data. In KDD '02 (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Montague, M., and Aslam, J. A. Condorcet fusion for improved retrieval. In CIKM (2002). Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Voorhees, E. M., and Harman, D. K., Eds. TREC -- Experiment and Evaluation in IR. MIT Press, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Xu, J., and Li, H. Adarank: a boosting algorithm for information retrieval. In SIGIR '07 (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Reciprocal rank fusion outperforms condorcet and individual rank learning methods

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    • Published in

      cover image ACM Conferences
      SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
      July 2009
      896 pages
      ISBN:9781605584836
      DOI:10.1145/1571941

      Copyright © 2009 Copyright is held by the author/owner(s)

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 19 July 2009

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      Overall Acceptance Rate792of3,983submissions,20%

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