Reciprocal rank fusion outperforms condorcet and individual rank learning methods

  title={Reciprocal rank fusion outperforms condorcet and individual rank learning methods},
  author={Gordon V. Cormack and Charles L. A. Clarke and Stefan B{\"u}ttcher},
  journal={Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval},
  • G. Cormack, C. Clarke, Stefan Büttcher
  • Published 19 July 2009
  • Computer Science
  • Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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 

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