Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
A statistical method for system evaluation using incomplete judgments
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation over thousands of queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions
ECIR'07 Proceedings of the 29th European conference on IR research
Using PageRank to infer user preferences
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Alternative assessor disagreement and retrieval depth
Proceedings of the 21st ACM international conference on Information and knowledge management
Document features predicting assessor disagreement
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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We propose a simple method for converting many standard measures of retrieval performance into metasearch algorithms. Our focus is both on the analysis of retrieval measures themselves and on the development of new metasearch algorithms. Given the conversion method proposed, our experimental results using TREC data indicate that system-oriented measures of overall retrieval performance (such as average precision) yield good metasearch algorithms whose performance equals or exceeds that of benchmark techniques such as CombMNZ and Condorcet.