Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Fusion Via a Linear Combination of Scores
Information Retrieval
Modeling score distributions for combining the outputs of search engines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
QuASM: a system for question answering using semi-structured data
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
The effectiveness of combining information retrieval strategies for European languages
Proceedings of the 2004 ACM symposium on Applied computing
ProbFuse: a probabilistic approach to data fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A formal approach to score normalization for meta-search
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Probability-based fusion of information retrieval result sets
Artificial Intelligence Review
Probabilistic data fusion on a large document collection
Artificial Intelligence Review
Generative model-based metasearch for data fusion in information retrieval
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Segmentation of search engine results for effective data-fusion
ECIR'07 Proceedings of the 29th European conference on IR research
Extending probabilistic data fusion using sliding windows
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
A fuzzy integral method to merge search engine results on web
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Fusion for multimodal biometric identification
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Copulas for information retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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We introduce a new, probabilistic model for combining the outputs of an arbitrary number of query retrieval systems. By gathering simple statistics on the average performance of a given set of query retrieval systems, we construct a Bayes optimal mechanism for combining the outputs of these systems. Our construction yields a metasearch strategy whose empirical performance nearly always exceeds the performance of any of the constituent systems. Our construction is also robust in the sense that if “good” and “bad” systems are combined, the Performance of the composite is still on par with, or exceeds, that of the best constituent system. Finally, our model and theory provide theoretical and empirical avenues for the improvement of this metasearch strategy.