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Score standardization for inter-collection comparison of retrieval systems
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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Singapore, Singapore
SESSION: Evaluation--1 table of contents
Pages: 51-58  
Year of Publication: 2008
ISBN:978-1-60558-164-4
Authors
William Webber  The University of Melbourne, Melbourne, Australia
Alistair Moffat  The University of Melbourne, Melbourne, Australia
Justin Zobel  The University of Melbourne, Melbourne, Australia
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 21,   Downloads (12 Months): 206,   Citation Count: 10
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ABSTRACT

The goal of system evaluation in information retrieval has always been to determine which of a set of systems is superior on a given collection. The tool used to determine system ordering is an evaluation metric such as average precision, which computes relative, collection-specific scores. We argue that a broader goal is achievable. In this paper we demonstrate that, by use of standardization, scores can be substantially independent of a particular collection, allowing systems to be compared even when they have been tested on different collections. Compared to current methods, our techniques provide richer information about system performance, improved clarity in outcome reporting, and greater simplicity in reviewing results from disparate sources.


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.

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C. Buckley. The SMART project at TREC. In Voorhees and Harman {2005}, chapter 13.
 
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G. Marchionini, A. Moffat, J. Tait, R. Baeza-Yates, and N. Ziviani, editors. Proc. 28th Ann. Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, Salvador, Brazil, August 2005.
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A. Moffat and J. Zobel. Rank-biased precision for measurement of retrieval effectiveness. ACM Trans. Inf. Syst., to appear.
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J. Tague-Sutcliffe and J. Blustein. A statistical analysis of the TREC-3 data. In D. K. Harman, editor, Proc. TREC-3, pages 385--398, November 1994. NIST Special Publication 500-225.
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W. Webber, A. Moffat, and J. Zobel. Score standardization for robust comparison of retrieval systems. In M.Wu, A. Turpin, and A. Spink, editors, Proc. 12th Australasian Document Computing Symposium, pages 1--8, Melbourne, December 2007.
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CITED BY  10

Collaborative Colleagues:
William Webber: colleagues
Alistair Moffat: colleagues
Justin Zobel: colleagues