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In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items.
Static indexing pruning for information retrieval systems
D. Carmel, D. Cohen, R. Fagin, E. Farchi, M. Herscovici, Y. Maarek, and A. Soffer · 2001
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Greedy function approximation: a gradient boosting machine
J. H. Friedman · 2001
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Rapid object detection using a boosted cascade of simple features
P. Viola and M. Jones · 2001
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Amazon. com recommendations: Item-to-item collaborative filtering
G. Linden, B. Smith, and J. York · 2003
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Feature-centric evaluation for efficient cascaded object detection
H. Schneiderman · 2004
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Robust object detection via soft cascade
L. Bourdev and J. Brandt · 2005
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The impact of caching on search engines
R. Baeza-Yates, A. Gionis, F. Junqueira, V. Murdock, V. Plachouras, and F. Silvestri · 2007
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Scaling up all pairs similarity search
R. J. Bayardo, Y. Ma, and R. Srikant · 2007
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Feature selection for rankng
X. Geng, T. Liu, T. Qin, and H. Li · 2007
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Cost-sensitive feature acquisition and classification
S. Ji and L. Carin · 2007
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Partial example acquisition in cost-sensitive learning
V. S. Sheng and C. X. Ling · 2007
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Learning classifiers from only positive and unlabeled data
C. Elkan and K. Noto · 2008
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Learning to rank for information rerieval
T. Liu · 2009
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Joint cascade optimization using a product of boosted classifiers
L. Lefakis and F. Fleuret · 2010
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Ranking under temporal constraints
L. Wang, D. Metzler, and J. Lin · 2010
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Yahoo! learning to rank challenge overview
O. Chapelle and Y. Chang · 2011
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A cascade ranking model for efficientcient ranked retrieval
L. Wang, J. Lin, and D. Metzler · 2011
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Classifier cascade for minimizing feature evaluation cost
M. Chen, Z. E.Xu, and K. Q. W. et al · 2012
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Activity ranking in linkedin feed
D. Agarwal, B.-C. Chen, R. Gupta, J. Hartman, Q. He, A. Iyer, S. Kolar, Y. Ma, P. Shivaswamy, A. Singh, et al · 2014
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Learning to efficiently rank on big data
L. Wang, J. Lin, D. Metzler, and J. Han · 2014
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V. C. Raykar, B. Krishnapuram, and S. Yu · 2010
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Learning to efficientciently rank
L. Wang, J. Lin, and D. Metzler · 2010
Cited alongside, same era.
Wide & deep learning for recommender systems
H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, et al · 2016
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Deep neural networks for youtube recommendations
P. Covington, J. Adams, and E. Sargin · 2016
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