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Search is at the heart of modern e-commerce.
Large margin rank boundaries for ordinal regression
R. Herbrich, T. Graepel, and K. Obermayer · 1999
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Cumulated gain-based evaluation of ir techniques
K. Järvelin and J. Kekäläinen · 2002
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Optimizing search engines using clickthrough data
T. Joachims · 2002
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Learning to rank using gradient descent
C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender · 2005
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Minimally invasive randomization for collecting unbiased preferences from clickthrough logs
F. Radlinski and T. Joachims · 2006
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Feature hashing for large scale multitask learning
K. Weinberger, A. Dasgupta, J. Langford, A. Smola, and J. Attenberg · 2009
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Learning to rank with (a lot of) word features
B. Bai, J. Weston, D. Grangier, R. Collobert, K. Sadamasa, Y. Qi, O. Chapelle, and K. Weinberger · 2010
Cited alongside, same era.
A survey on transfer learning
S. J. Pan and Q. Yang · 2010
Cited alongside, same era.
Tabula rasa: Model transfer for object category detection
Y. Aytar and A. Zisserman · 2011
Cited alongside, same era.
A short introduction to learning to rank
L. Hang · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Later among the works it cites.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Learning categories from few examples with multi model knowledge transfer
T. Tommasi, F. Orabona, and B. Caputo · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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L. A. Gatys, A. S. Ecker, and M. Bethge · 2015
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