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» Listwise approach to learning to rank: theory and algorithm
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KDD
2007
ACM
192views Data Mining» more  KDD 2007»
14 years 9 months ago
Active exploration for learning rankings from clickthrough data
We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. ...
Filip Radlinski, Thorsten Joachims
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 9 months ago
Real-time ranking with concept drift using expert advice
In many practical applications, one is interested in generating a ranked list of items using information mined from continuous streams of data. For example, in the context of comp...
Hila Becker, Marta Arias
CIKM
2011
Springer
12 years 9 months ago
Improved answer ranking in social question-answering portals
Community QA portals provide an important resource for non-factoid question-answering. The inherent noisiness of user-generated data makes the identification of high-quality cont...
Felix Hieber, Stefan Riezler
SIGIR
2011
ACM
12 years 11 months ago
Active learning to maximize accuracy vs. effort in interactive information retrieval
We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial documen...
Aibo Tian, Matthew Lease
DBWORKSHOPS
1992
13 years 10 months ago
An Incremental Concept Formation Approach for Learning from Databases
Godin, R. and R. Missaoui, An incremental concept formation approach for learning from databases, Theoretical Computer Science 133 (1994) 3533385. This paper describes a concept f...
Rokia Missaoui, Robert Godin