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Online content platforms commonly use engagement-based optimization when making recommendations.
Monopolistic competition with outside goods
Steven C. Salop · 1979
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Stability in competition
Harold Hotelling · 1981
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Discrete Choice Theory of Product Differentiation
Simon P. Anderson, Andre de Palma, and Jacques-Francois Thisse · 1992
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On the existence of pure and mixed strategy nash equilibria in discontinuous games
Philip J. Reny · 1999
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Incentivizing high-quality user-generated content
Arpita Ghosh and R. Preston McAfee · 2011
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Static prediction games for adversarial learning problems
Michael Brückner, Christian Kanzow, and Tobias Scheffer · 2012
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Learning and incentives in user-generated content: multi-armed bandits with endogenous arms
Arpita Ghosh and Patrick Hummel · 2013
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Implementing the ”wisdom of the crowd”
Ilan Kremer, Yishay Mansour, and Motty Perry · 2013
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Incentivizing exploration
Peter I. Frazier, David Kempe, Jon M. Kleinberg, and Robert Kleinberg · 2014
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Social status and badge design
Nicole Immorlica, Gregory Stoddard, and Vasilis Syrgkanis · 2015
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Behaviorism is not enough: Better recommendations through listening to users
Michael D. Ekstrand and Martijn C. Willemsen · 2016
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Strategic classification
Moritz Hardt, Nimrod Megiddo, Christos Papadimitriou, and Mary Wootters · 2016
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A game theoretic analysis of the adversarial retrieval setting
Ran Ben Basat, Moshe Tennenholtz, and Oren Kurland · 2017
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A game-theoretic approach to recommendation systems with strategic content providers
Omer Ben-Porat and Moshe Tennenholtz · 2018
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Incentivizing high quality user contributions: New arm generation in bandit learning
Yang Liu and Chien-Ju Ho · 2018
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How do classifiers induce agents to invest effort strategically?
Jon Kleinberg and Manish Raghavan · 2019
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Continuing our work to improve recommendations on youtube, 2019
YouTube · 2019
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Content provider dynamics and coordination in recommendation ecosystems
Omer Ben-Porat, Itay Rosenberg, and Moshe Tennenholtz · 2020
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Maximizing welfare with incentive-aware evaluation mechanisms
Nika Haghtalab, Nicole Immorlica, Brendan Lucier, and Jack Z. Wang · 2020
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Optimizing long-term social welfare in recommender systems: A constrained matching approach
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, and Craig Boutilier · 2020
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Angry by design: toxic communication and technical architectures
Luke Munn · 2020
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Performative prediction
Juan C. Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt · 2020
Supply-side equilibria in recommender systems
Meena Jagadeesan, Nikhil Garg, and Jacob Steinhardt · 2022
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The challenge of understanding what users want: Inconsistent preferences and engagement optimization
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2022
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Strategic ranking
Lydia T. Liu, Nikhil Garg, and Christian Borgs · 2022
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Digital content creation: An analysis of the impact of recommendation systems
Kun Qian and Sanjay Jain · 2022
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Learning with exposure constraints in recommendation systems
Omer Ben-Porat and Rotem Torkan · 2023
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Recommenders’ originals: The welfare effects of the dual role of platforms as producers and recommender systems, 8 2021
Guy Aridor and Duarte Gonçalves · 2021
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Strategic classification in the dark
Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, and Nir Rosenfeld · 2021
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From optimizing engagement to measuring value
Smitha Milli, Luca Belli, and Moritz Hardt · 2021
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Out-group animosity drives engagement on social media
Steve Rathje, Jay J. Van Bavel, and Sander van der Linden · 2021
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The price of incentivizing exploration: A characterization via thompson sampling and sample complexity
Mark Sellke and Aleksandrs Slivkins · 2021
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How tiktok reads your mind
Ben Smith · 2021
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Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis, and Haifeng Xu · 2023
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Artificial intelligence, algorithmic recommendations and competition
Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, and Sergio Pastorello · 2023
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Recommender systems and competition on subscription-based platforms
Jacopo Castellini, Amelia Fletcher, Peter L. Ormosi, and Rahul Savani · 2023
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Recommending to strategic users
Andreas A. Haupt, Dylan Hadfield-Menell, and Chara Podimata · 2023
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Modeling content creator incentives on algorithm-curated platforms
Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, and Sarah Dean · 2023
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Incentivizing high-quality content in online recommender systems
Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, and Jacob Steinhardt · 2023
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Siddharth Prasad, Martin Mladenov, and Craig Boutilier · 2023
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Twitter’s recommendation algorithm
Twitter · 2023
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How bad is top-k recommendation under competing content creators?
Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, and Haifeng Xu · 2023
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Online learning in a creator economy
Banghua Zhu, Sai Praneeth Karimireddy, Jiantao Jiao, and Michael I. Jordan · 2023
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Matching of users and creators in two-sided markets with departures
Daniel P. Huttenlocher, Hannah Li, Liang Lyu, Asuman E. Ozdaglar, and James Siderius · 2024
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