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Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems.
A generalization of sampling without replacement from a finite universe
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Report on the need for and provision of an “ideal” information retrieval test collection
K. Sparck-Jones and C. J. V. Rijsbergen · 1975
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The central role of the propensity score in observational studies for causal effects
P. R. Rosenbaum and D. B. Rubin · 1983
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Statistical Learning Theory
V. Vapnik · 1998
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Optimizing search engines using clickthrough data
T. Joachims · 2002
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Statistical Analysis with Missing Data
R. J. A. Little and D. B. Rubin · 2002
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Training linear SVMs in linear time
T. Joachims · 2006
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Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search
T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay · 2007
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Predicting clicks: Estimating the click-through rate for new ads
M. Richardson, E. Dominowska, and R. Ragno · 2007
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An experimental comparison of click position-bias models
N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey · 2008
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A dynamic bayesian network click model for web search ranking
O. Chapelle and Y. Zhang · 2009
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Learning to rank for information retrieval
T.-Y. Liu · 2009
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Interactively optimizing information retrieval systems as a dueling bandits problem
Y. Yue and T. Joachims · 2009
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Learning from logged implicit exploration data
A. L. Strehl, J. Langford, L. Li, and S. Kakade · 2010
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Beyond position bias: examining result attractiveness as a source of presentation bias in clickthrough data
Y. Yue, R. Patel, and H. Roehrig · 2010
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Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
L. Li, W. Chu, J. Langford, and X. Wang · 2011
Stable coactive learning via perturbation
K. Raman, T. Joachims, P. Shivaswamy, and T. Schnabel · 2013
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Click Models for Web Search
A. Chuklin, I. Markov, and M. de Rijke · 2015
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Causal Inference for Statistics, Social, and Biomedical Sciences
G. Imbens and D. Rubin · 2015
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Batch learning from logged bandit feedback through counterfactual risk minimization
A. Swaminathan and T. Joachims · 2015
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A neural click model for web search
A. Borisov, I. Markov, M. de Rijke, and P. Serdyukov · 2016
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Unbiased comparative evaluation of ranking functions
T. Schnabel, A. Swaminathan, P. Frazier, and T. Joachims · 2016
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A cascade ranking model for efficient ranked retrieval
L. Wang, J. J. Lin, and D. Metzler · 2011
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Large-scale validation and analysis of interleaved search evaluation
O. Chapelle, T. Joachims, F. Radlinski, and Y. Yue · 2012
Cited alongside, same era.
Reusing historical interaction data for faster online learning to rank for ir
K. Hofmann, A. Schuth, S. Whiteson, and M. de Rijke · 2013
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Learning socially optimal information systems from egoistic users
K. Raman and T. Joachims · 2013
Cited alongside, same era.
Recommendations as treatments: Debiasing learning and evaluation
T. Schnabel, A. Swaminathan, A. Singh, N. Chandak, and T. Joachims · 2016
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Multileave gradient descent for fast online learning to rank
A. Schuth, H. Oosterhuis, S. Whiteson, and M. de Rijke · 2016
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Learning to rank with selection bias in personal search
X. Wang, M. Bendersky, D. Metzler, and M. Najork · 2016
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Beyond ranking: Optimizing whole-page presentation
Y. Wang, D. Yin, L. Jie, P. Wang, M. Yamada, Y. Chang, and Q. Mei · 2016
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