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SIGIR
2008
ACM
13 years 4 months ago
Directly optimizing evaluation measures in learning to rank
One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures used in i...
Jun Xu, Tie-Yan Liu, Min Lu, Hang Li, Wei-Ying Ma
DEXAW
2010
IEEE
196views Database» more  DEXAW 2010»
13 years 4 months ago
Direct Optimization of Evaluation Measures in Learning to Rank Using Particle Swarm
— One of the central issues in Learning to Rank (L2R) for Information Retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures ...
Ósscar Alejo, Juan M. Fernández-Luna...
CIKM
2009
Springer
13 years 11 months ago
Learning to rank from Bayesian decision inference
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang
ECIR
2010
Springer
13 years 2 months ago
Maximum Margin Ranking Algorithms for Information Retrieval
Abstract. Machine learning ranking methods are increasingly applied to ranking tasks in information retrieval (IR). However ranking tasks in IR often differ from standard ranking t...
Shivani Agarwal, Michael Collins
ICASSP
2009
IEEE
13 years 8 months ago
Improved lattice-based spoken document retrieval by directly learning from the evaluation measures
Lattice-based approaches have been widely used in spoken document retrieval to handle the speech recognition uncertainty and errors. Position Specific Posterior Lattices (PSPL) an...
Chao-hong Meng, Hung-yi Lee, Lin-shan Lee