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Evaluation measures for preference judgments
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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
POSTER SESSION: Posters group 1: evaluation, text collections and user/personalized IR table of contents
Pages 685-686  
Year of Publication: 2008
ISBN:978-1-60558-164-4
Authors
Ben Carterette  University of Massachusetts Amherst, Amherst, MA, USA
Paul N. Bennett  Microsoft Research, Redmond, WA, USA
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

There has been recent interest in collecting user or assessor preferences, rather than absolute judgments of relevance, for the evaluation or learning of ranking algorithms. Since measures like precision, recall, and DCG are defined over absolute judgments, evaluation over preferences will require new evaluation measures that explicitly model them. We describe a class of such measures and compare absolute and preference measures over a large TREC collection.


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
B. Carterette, P. N. Bennett, D. M. Chickering, and S. T. Dumais. Here or there: Preference judgments for relevance. In Proceedings of ECIR, pages 16--27, 2008.
 
2
M. E. Rorvig. The simple scalability of documents. JASIS, 41(8):590--598, 1990.

Collaborative Colleagues:
Ben Carterette: colleagues
Paul N. Bennett: colleagues