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» Listwise approach to learning to rank: theory and algorithm
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WSDM
2012
ACM
267views Data Mining» more  WSDM 2012»
12 years 4 months ago
Learning to rank with multi-aspect relevance for vertical search
Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-d...
Changsung Kang, Xuanhui Wang, Yi Chang, Belle L. T...
SIGIR
2012
ACM
11 years 11 months ago
Robust ranking models via risk-sensitive optimization
Many techniques for improving search result quality have been proposed. Typically, these techniques increase average effectiveness by devising advanced ranking features and/or by...
Lidan Wang, Paul N. Bennett, Kevyn Collins-Thompso...
ECML
2006
Springer
14 years 19 days ago
A Discriminative Approach for the Retrieval of Images from Text Queries
This work proposes a new approach to the retrieval of images from text queries. Contrasting with previous work, this method relies on a discriminative model: the parameters are sel...
David Grangier, Florent Monay, Samy Bengio
ICML
1998
IEEE
14 years 9 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
NIPS
2001
13 years 10 months ago
The Steering Approach for Multi-Criteria Reinforcement Learning
We consider the problem of learning to attain multiple goals in a dynamic environment, which is initially unknown. In addition, the environment may contain arbitrarily varying ele...
Shie Mannor, Nahum Shimkin