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Universal Approximation Functions for Fast Learning to Rank: Replacing Expensive Regression Forests with Simple Feed-Forward Networks.
Daniel Cohen
John Foley
Hamed Zamani
James Allan
W. Bruce Croft
Published in:
SIGIR (2018)
Keyphrases
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learning to rank
ranking functions
loss function
information retrieval
document retrieval
evaluation measures
ranking svm
direct optimization
regression forests
supervised learning
data sets
learning to rank algorithms
pairwise
collaborative filtering
feature set
face recognition
feature selection