Reciprocal rank fusion outperforms condorcet and individual rank learning methods

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Reciprocal rank fusion outperforms condorcet and individual rank learning methods

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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval table of contents

Boston, MA, USA

POSTER SESSION: Posters table of contents

Pages: 758-759

Year of Publication: 2009

ISBN:978-1-60558-483-6

Authors

Gordon V. Cormack	University of Waterloo, Waterloo, ON, Canada
Charles L A Clarke	University of Waterloo, Waterloo, ON, Canada
Stefan Buettcher	Google, Mountain View, CA, USA

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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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

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Zhe Cao , Tao Qin , Tie-Yan Liu , Ming-Feng Tsai , Hang Li, Learning to rank: from pairwise approach to listwise approach, Proceedings of the 24th international conference on Machine learning, p.129-136, June 20-24, 2007, Corvalis, Oregon [doi>10.1145/1273496.1273513]
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