- Learning to rank from relevance feedback for e-discovery
- Lecture Notes in Computer Science
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
- In recall-oriented search tasks retrieval systems are privy to a greater amount of user feedback. In this paper we present a novel method of combining relevance feedback with learning to rank. Our experiments use data from the 2010 TREC Legal track to demonstrate that learning to rank can tune relevance feedback to improve result rankings for specific queries, even with limited amounts of user feedback.
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- Proceedings title: Advances in information retrieval: 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain,
April 1-5, 2012. Proceedings
Place of publication: Berlin
Editors: R. Baeza-Yates, A.P. de Vries, H. Zaragoza, B.B. Cambazoglu, V. Murdock, R. Lempel, F. Silvestri
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