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» Conditional Models for Non-smooth Ranking Loss Functions
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ICDM
2009
IEEE
100views Data Mining» more  ICDM 2009»
14 years 4 days ago
Conditional Models for Non-smooth Ranking Loss Functions
Avinava Dubey, Jinesh Machchhar, Chiranjib Bhattac...
WSDM
2010
ACM
194views Data Mining» more  WSDM 2010»
14 years 2 months ago
Ranking with Query-Dependent Loss for Web Search
Queries describe the users' search intent and therefore they play an essential role in the context of ranking for information retrieval and Web search. However, most of exist...
Jiang Bian, Tie-Yan Liu, Tao Qin, Hongyuan Zha
IPM
2008
100views more  IPM 2008»
13 years 5 months ago
Query-level loss functions for information retrieval
Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since...
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng W...
NIPS
2008
13 years 7 months ago
Global Ranking Using Continuous Conditional Random Fields
This paper studies global ranking problem by learning to rank methods. Conventional learning to rank methods are usually designed for `local ranking', in the sense that the r...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang...
KDD
2012
ACM
201views Data Mining» more  KDD 2012»
11 years 8 months ago
Low rank modeling of signed networks
Trust networks, where people leave trust and distrust feedback, are becoming increasingly common. These networks may be regarded as signed graphs, where a positive edge weight cap...
Cho-Jui Hsieh, Kai-Yang Chiang, Inderjit S. Dhillo...