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ProbFuse: a probabilistic approach to data fusion
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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
SESSION: Fusion and spam table of contents
Pages: 139 - 146  
Year of Publication: 2006
ISBN:1-59593-369-7
Authors
David Lillis  University College Dublin
Fergus Toolan  University College Dublin
Rem Collier  University College Dublin
John Dunnion  University College Dublin
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 9,   Downloads (12 Months): 103,   Citation Count: 10
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ABSTRACT

Data fusion is the combination of the results of independent searches on a document collection into one single output result set. It has been shown in the past that this can greatly improve retrieval effectiveness over that of the individual results.This paper presents probFuse, a probabilistic approach to data fusion. ProbFuse assumes that the performance of the individual input systems on a number of training queries is indicative of their future performance. The fused result set is based on probabilities of relevance calculated during this training process. Retrieval experiments using data from the TREC ad hoc collection demonstrate that probFuse achieves results superior to that of the popular CombMNZ fusion algorithm.


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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D. Lillis, F. Toolan, A. Mur, L. Peng, R. Collier, and J. Dunnion. Probability-based fusion of information retrieval result sets. In Proceedings of the 16th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2005), pages 147--156, Portstewart, Northern Ireland, 2005. University of Ulster.
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CITED BY  10

Collaborative Colleagues:
David Lillis: colleagues
Fergus Toolan: colleagues
Rem Collier: colleagues
John Dunnion: colleagues