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Personalization is pervasive in the online space as, when combined with learning, it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user.
Scaling personalized web search
Glen Jeh and Jennifer Widom · 2003
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Handling advertisements of unknown quality in search advertising
Sandeep Pandey and Christopher Olston · 2006
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In google we trust: Users? decisions on rank, position, and relevance
Bing Pan, Helene Hembrooke, Thorsten Joachims, Lori Lorigo, Geri Gay, and Laura Granka · 2007
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Stochastic linear optimization under bandit feedback
Varsha Dani, Thomas P Hayes, and Sham M Kakade · 2008
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Personalized social search based on the user’s social network
David Carmel, Naama Zwerdling, Ido Guy, Shila Ofek-Koifman, Nadav Har’El, Inbal Ronen, Erel Uziel, Sivan Yogev, and Sergey Chernov · 2009
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Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
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Ad exchanges: Research issues
S Muthukrishnan · 2009
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Personalized news recommendation based on click behavior
Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen · 2010
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Online display advertising: Targeting and obtrusiveness
Avi Goldfarb and Catherine Tucker · 2011
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Regret analysis of stochastic and nonstochastic multi-armed bandit problems
Sébastien Bubeck, Nicolo Cesa-Bianchi, et al · 2012
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How effective is targeted advertising?
Ayman Farahat and Michael C Bailey · 2012
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Discrimination in online ad delivery
Latanya Sweeney · 2013
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Automated experiments on ad privacy settings
Amit Datta, Michael Carl Tschantz, and Anupam Datta · 2015
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The search engine manipulation effect (seme) and its possible impact on the outcomes of elections
Robert Epstein and Ronald E Robertson · 2015
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Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Linear contextual bandits with knapsacks
Shipra Agrawal and Nikhil Devanur · 2016
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How to be fair and diverse?
L. Elisa Celis, Amit Deshpande, Tarun Kathuria, and Nisheeth K Vishnoi · 2016
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Introduction to online convex optimization
Elad Hazan et al · 2016
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Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 2016
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Stochastic contextual bandits with known reward functions
Pranav Sakulkar and Bhaskar Krishnamachari · 2016
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Measuring Fairness in Ranked Outputs
Ke Yang and Julia Stoyanovich · 2016
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Facebook Top Source for Political News Among Millennials, June 2015
Amy Mitchell, Jeffrey Gottfried, and Katerina Eva Matsa · 2015
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