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cormacksigir09-rrf.pdf
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Summary

Reciprocal Rank Fusion (RRF) outperforms Condorcet and individual rank learning methods, consistently yielding better results than any individual system. RRF combines document rankings from multiple IR systems using a simple scoring formula, and it equals or betters other methods, including Condorcet Fuse and CombMNZ. Experiments on TREC collections and the LETOR 3 dataset show that RRF outperforms Condorcet, CombMNZ, and the best system by 4% to 5% on average, with significant differences. RRF is simpler and more effective than Condorcet Fuse, and it requires no special voting algorithm or global information.

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Description

Reciprocal Rank Fusion (RRF) outperforms Condorcet and individual rank learning methods, consistently yielding better results than any individual system.

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  • File Format: PDF
  • File Size: 64 KB
  • Pages: 2
  • Language: EN
  • Total Downloads: 42
  • Last Updated: 6 months ago

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