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Web page ranking and collaborative filtering require the optimization of sophisticated performance measures.
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The TREC-9 filtering track final report
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On the complexity of learning the kernel matrix
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Pranking with ranking
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IR evaluation methods for retrieving highly relevant documents
K. Jarvelin and J. Kekalainen · 2002
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Optimizing search engines using clickthrough data
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K. Crammer and Y. Singer · 2005
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A support vector method for multivariate performance measures
T. Joachims · 2005
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Fast maximum margin matrix factoriazation for collaborative prediction
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Rank, trace-norm and max-norm
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Large margin methods for structured and interdependent output variables
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Hyperkernels
C. S. Ong, A. J. Smola, and R. C. Williamson · 2003
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Unifying collaborative and content-based filtering
J. Basilico and T. Hofmann · 2004
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Max-margin Markov networks
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An algorithmic framework for convex mixed integer nonlinear programs
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Learning to rank using gradient descent
C.J.C Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hulldender · 2005
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Subset ranking using regression
D. Cossock and T. Zhang · 2006
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High accuracy retrieval with multiple nested ranker
I. Matveeva, C. Burges, T. Burkard, A. Laucius, and L. Wong · 2006
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Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
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Beyond pagerank: machine learning for static ranking
M. Richardson, A. Prakash, and E. Brill · 2006
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Ranking with a P-norm push
C. Rudin · 2006
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Online learning meets optimization in the dual
S. Shalev-Shwartz and Y. Singer · 2006
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Collaborative ordinal regression
S. Yu, K. Yu, V. Tresp, and H. P. Kriegel · 2006
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Learning to rank with nonsmooth cost functions
C. J. Burges, Q. V. Le, and R. Ragno · 2007
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