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CIKM
2010
Springer
13 years 8 months ago
Learning to rank relevant and novel documents through user feedback
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
Abhimanyu Lad, Yiming Yang
IFIP12
2009
13 years 7 months ago
Preferential Infinitesimals for Information Retrieval
In this paper, we propose a preference framework for information retrieval in which the user and the system administrator are enabled to express preference annotations on search ke...
Maria Chowdhury, Alex Thomo, William W. Wadge
ECML
2006
Springer
14 years 1 months ago
Cost-Sensitive Learning of SVM for Ranking
Abstract. In this paper, we propose a new method for learning to rank. `Ranking SVM' is a method for performing the task. It formulizes the problem as that of binary classific...
Jun Xu, Yunbo Cao, Hang Li, Yalou Huang
CICLING
2011
Springer
13 years 1 months ago
Ranking Multilingual Documents Using Minimal Language Dependent Resources
This paper proposes an approach of extracting simple and effective features that enhances multilingual document ranking (MLDR). There is limited prior research on capturing the co...
G. S. K. Santosh, N. Kiran Kumar, Vasudeva Varma
AAAI
2008
13 years 12 months ago
Semi-Supervised Ensemble Ranking
Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance b...
Steven C. H. Hoi, Rong Jin