Data fusion with estimated weights

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Data fusion with estimated weights

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Conference on Information and Knowledge Management archive
Proceedings of the eleventh international conference on Information and knowledge management table of contents

McLean, Virginia, USA

SESSION: Poster session table of contents

Pages: 648 - 651

Year of Publication: 2002

ISBN:1-58113-492-4

Authors

Shengli Wu	University of Strathclyde, Glasgow, UK
Fabio Crestani	University of Strathclyde, Glasgow, UK

Sponsors

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

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 6, Downloads (12 Months): 55, Citation Count: 7

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ABSTRACT

This paper proposes an adptive approach for data fusion of information retrieval systems, which exploits estimated performances of all component input systems without relevance judgement or training. The estimation is conducted prior to the fusion but uses the same data as fusion applies. The experiment shows that our algorithms are competitive with, and often outperform CombMNZ, one of the most effective algorithms in use.

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	D. K. Harman, editor. Proceedings of 3rd Text Retrieval Conference (TREC-3), Gaithersburg, Maryland, USA, April 1995. National Technical Information Service of USA.
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	4	Javed A. Aslam , Mark Montague, Models for metasearch, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.276-284, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.384007]
	5	Ian Soboroff , Charles Nicholas , Patrick Cahan, Ranking retrieval systems without relevance judgments, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.66-73, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383961]
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CITED BY 7

	Shengli Wu , Fabio Crestani, Methods for ranking information retrieval systems without relevance judgments, Proceedings of the 2003 ACM symposium on Applied computing, March 09-12, 2003, Melbourne, Florida
	Shengli Wu , Sally McClean, Performance prediction of data fusion for information retrieval, Information Processing and Management: an International Journal, v.42 n.4, p.899-915, July 2006
	D. Lillis , F. Toolan , A. Mur , L. Peng , R. Collier , J. Dunnion, Probability-based fusion of information retrieval result sets, Artificial Intelligence Review, v.25 n.1-2, p.179-191, April 2006
	Shengli Wu , Sally McClean, Improving high accuracy retrieval by eliminating the uneven correlation effect in data fusion, Journal of the American Society for Information Science and Technology, v.57 n.14, p.1962-1973, December 2006
	Anselm Spoerri, Using the structure of overlap between search results to rank retrieval systems without relevance judgments, Information Processing and Management: an International Journal, v.43 n.4, p.1059-1070, July, 2007
	Noureddine Abbadeni, Information retrieval from visual databases using multiple representations and multiple queries, Proceedings of the 2009 ACM symposium on Applied Computing, March 08-12, 2009, Honolulu, Hawaii
	Shengli Wu , Yaxin Bi , Xiaoqin Zeng , Lixin Han, Assigning appropriate weights for the linear combination data fusion method in information retrieval, Information Processing and Management: an International Journal, v.45 n.4, p.413-426, July, 2009