Association for Computing Machinery (ACM)

Reciprocal rank fusion outperforms condorcet and individual rank learning methods

Тип публикации: Proceedings Article

Дата публикации: 2009-07-19

Association for Computing Machinery (ACM)

DOI: 10.1145/1571941.1572114

Краткое описание

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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