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How to leverage cross-document interactions to improve ranking performance is an important topic in information retrieval (IR) research.
Recall, precision and average precision
Mu Zhu · 1910
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Optimum polynomial retrieval functions based on the probability ranking principle
Norbert Fuhr · 1989
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Cumulated gain-based evaluation of IR techniques
Kalervo Järvelin and Jaana Kekäläinen · 2002
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Optimizing search engines using clickthrough data
Thorsten Joachims · 2002
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Learning to rank using gradient descent
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender · 2005
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Preference learning with gaussian processes
Wei Chu and Zoubin Ghahramani · 2005
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Learning to rank with nonsmooth cost functions
Christopher J. C. Burges, Robert Ragno, and Quoc Viet Le · 2006
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Training linear svms in linear time
Thorsten Joachims · 2006
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Learning to rank: from pairwise approach to listwise approach
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li · 2007
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Regularizing query-based retrieval scores
Fernando Diaz · 2007
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Adarank: A boosting algorithm for information retrieval
Jun Xu and Hang Li · 2007
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Global ranking using continuous conditional random fields
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, and Hang Li · 2008
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Mean reciprocal rank
Nick Craswell · 2009
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Learning to rank for information retrieval
Tie-Yan Liu · 2009
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From RankNet to LambdaRank to LambdaMART: An overview
Christopher J.C. Burges · 2010
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Rectified linear units improve restricted Boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Adapting boosting for information retrieval measures
Qiang Wu, Christopher JC Burges, Krysta M Svore, and Jianfeng Gao · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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The Lemur project
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Learning deep structured semantic models for web search using clickthrough data
Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck · 2013
LightGBM: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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A structured self-attentive sentence embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio · 2017
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Learning to match using local and distributed representations of text for web search
Bhaskar Mitra, Fernando Diaz, and Nick Craswell · 2017
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Deeprank: A new deep architecture for relevance ranking in information retrieval
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Jingfang Xu, and Xueqi Cheng · 2017
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Quick Access: Building a smart experience for Google Drive
Sandeep Tata, Alexandrin Popescul, Marc Najork, Mike Colagrosso, Julian Gibbons, Alan Green, Alexandre Mah, Michael Smith, Divanshu Garg, Cayden Meyer, et al · 2017
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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A neural click model for web search
Alexey Borisov, Ilya Markov, Maarten de Rijke, and Pavel Serdyukov · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Text matching as image recognition
Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, and Xueqi Cheng · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
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Learning a deep listwise context model for ranking refinement
Qingyao Ai, Keping Bi, Jiafeng Guo, and W Bruce Croft · 2018
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Seq2slate: Re-ranking and slate optimization with rnns
Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Chi, Elad Eban, Xiyang Luo, Alan Mackey, and Ofer Meshi · 2018
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Attention-based deep multiple instance learning
Maximilian Ilse, Jakub M Tomczak, and Max Welling · 2018
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Learning groupwise multivariate scoring functions using deep neural networks
Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Mike Bendersky, and Marc Najork · 2019
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An analysis of the softmax cross entropy loss for learning-to-rank with binary relevance
Sebastian Bruch, Xuanhui Wang, Mike Bendersky, and Marc Najork · 2019
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A deep look into neural ranking models for information retrieval
Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W Bruce Croft, and Xueqi Cheng · 2019
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Tf-ranking: Scalable tensorflow library for learning-to-rank
Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang, Cheng Li, Michael Bendersky, Marc Najork, Jan Pfeifer, Nadav Golbandi, Rohan Anil, and Stephan Wolf · 2019
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Personalized re-ranking for recommendation
Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, et al · 2019
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