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» Learning to rank for information retrieval (LR4IR 2008)
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TREC
2000
15 years 7 months ago
Support for Question-Answering in Interactive Information Retrieval: Rutgers' TREC-9 Interactive Track Experience
We compared two different interfaces to the InQuery IR system with respect to their support for the TREC-9 Interactive Track Question-Answering task. One interface presented searc...
Nicholas J. Belkin, Amymarie Keller, Diane Kelly, ...
WWW
2009
ACM
16 years 6 months ago
Learning consensus opinion: mining data from a labeling game
We consider the problem of identifying the consensus ranking for the results of a query, given preferences among those results from a set of individual users. Once consensus ranki...
Paul N. Bennett, David Maxwell Chickering, Anton M...
CIKM
2009
Springer
15 years 10 months ago
Heterogeneous cross domain ranking in latent space
Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in m...
Bo Wang, Jie Tang, Wei Fan, Songcan Chen, Zi Yang,...
CIKM
2010
Springer
15 years 4 months ago
Wisdom of the ages: toward delivering the children's web with the link-based agerank algorithm
Though children frequently use web search engines to learn, interact, and be entertained, modern web search engines are poorly suited to children's needs, requiring relativel...
Karl Gyllstrom, Marie-Francine Moens
CIKM
2008
Springer
15 years 8 months ago
Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
We introduce a multi-stage ensemble framework, ErrorDriven Generalist+Expert or Edge, for improved classification on large-scale text categorization problems. Edge first trains a ...
Jian Huang 0002, Omid Madani, C. Lee Giles