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Quantative analysis of the impact of judging inconsistency on the performance of relevance feedback
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Seattle, Washington, USA
POSTER SESSION: Posters table of contents
Pages: 655 - 656  
Year of Publication: 2006
ISBN:1-59593-369-7
Authors
Xiangyu Jin  University of Virginia, Charlottesville, VA
James French  University of Virginia, Charlottesville, VA
Jonathan Michel  Science Applications International Corporation, Charlottesville, VA
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

Practical constrains of user interfaces make the user's judgment (during the feedback loop) deviate from real thoughts (when the full document is read).This is often overlooked in evaluation of relevance feedback.This paper quantitatively analyze the impact of judging inconsistency on the performance of relevance feedback.


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
X. Jin, J. French, and J. Michel. Saic and university of virginia at trec 2005: Hard track. In TREC 2005.
 
2
X. Jin, J. C. French, and J. Michel. Toward consistent evaluation of relevance feedback approaches in multimedia retrieval. In Adaptive Multimedia Retrieval pages 191--206, 2005.
 
3
J. Rocchio. Relevance feedback in information retrieval. In G. Salton, editor, The SMART Retrieval System: Experiments in Automatic Document Processing pages 313--323. Prentice-Hall, 1971.
 
4
G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. JASIS 41(4):288--297, 1990.
 
5
X. Shen and C.Zhai. Active feedback-UIUC TREC-2003 HARD experiments. In TREC pages 662--666, 2003.
 
6

Collaborative Colleagues:
Xiangyu Jin: colleagues
James French: colleagues
Jonathan Michel: colleagues