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Modern recommender systems lie at the heart of complex ecosystems that couple the behavior of users, content providers, advertisers, and other actors.
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Preference Ratios in Multiattribute Evaluation (PRIME)—Elicitation and Decision Procedures under Incomplete Information
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Interactive Critiquing for Catalog Navigation in E-Commerce
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Risk-sensitive Reinforcement Learning
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Getting to Know You: Learning New User Preferences in Recommender Systems
Rashid, A. M.; Albert, I.; Cosley, D.; Lam, S. K.; McNee, S. M.; Konstan, J. A.; and Riedl, J. 2002 · 2002
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Advances in Behavioral Economics
Camerer, C. F.; Loewenstein, G.; and Rabin, M., eds. 2003 · 2003
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Preference Elicitation and Query Learning
Blum, A.; Jackson, J. C.; Sandholm, T.; and Zinkevich, M. 2004 · 2004
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Applying Learning Algorithms to Preference Elicitation
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Revenue Management under a General Discrete Choice Model of Consumer Behavior
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Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis
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Mechanism Design via Machine Learning
Balcan, M.-F.; Blum, A.; Hartline, J. D.; and Mansour, Y. 2005 · 2005
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Combinatorial Auctions
Cramton, P.; Shoham, Y.; and Steinberg, R., eds. 2005 · 2005
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An MDP-based Recommender System
Shani, G.; Heckerman, D.; and Brafman, R. I. 2005 · 2005
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A Framework for Fairness in Two-Sided Marketplaces
Basu, K.; DiCiccio, C.; Logan, H.; and Karoui, N. E. 2020 · 2006
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Constraint-based Optimization and Utility Elicitation using the Minimax Decision Criterion
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Designing Economic Mechanisms
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The Construction of Preference
Lichtenstein, S.; and Slovic, P. 2006 · 2006
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Preference Elicitation in Combinatorial Auctions
Sandholm, T.; and Boutilier, C. 2006 · 2006
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Preference-based Search using Example-Critiquing with Suggestions
Viappiani, P.; Faltings, B.; and Pu, P. 2006 · 2006
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Mechanism Design with Partial Revelation
Hyafil, N.; and Boutilier, C. 2007 · 2007
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Evaluating the Accuracy of Implicit Feedback from Clicks and Query Reformulations in Web Search
Joachims, T.; Granka, L.; Pan, B.; Hembrooke, H.; Radlinski, F.; and Gay, G. 2007 · 2007
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Introduction to Mechanism Design (for Computer Scientists)
Nisan, N. 2007 · 2007
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Online Mechanisms
Parkes, D. C. 2007 · 2007
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Probabilistic Matrix Factorization
Salakhutdinov, R.; and Mnih, A. 2007 · 2007
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Automated Design of Multistage Mechanisms
Sandholm, T.; Conitzer, V.; and Boutilier, C. 2007 · 2007
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Mechanism Design without Money
Schummer, J.; and Vohra, R. V. 2007 · 2007
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Usage-based Web Recommendations: A Reinforcement Learning Approach
Taghipour, N.; Kardan, A.; and Ghidary, S. S. 2007 · 2007
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Multi-Agent Reinforcement Learning: An Overview
Busoniu, L.; Babuška, R.; and De Schutter, B. 2008 · 2008
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Collaborative Filtering for Implicit Feedback Datasets
Hu, Y.; Koren, Y.; and Volinsky, C. 2008 · 2008
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Personalized Interactive Faceted Ssearch
Koren, J.; Zhang, Y.; and Liu, X. 2008 · 2008
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Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Mladenov, M.; Creager, E.; Ben-Porat, O.; Swersky, K.; Zemel, R.; and Boutilier, C. 2020a · 2008
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User-involved Preference Elicitation for Product Search and Recommender Systems
Pu, P.; and Chen, L. 2008 · 2008
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Matrix Factorization Techniques for Recommender Systems
Improving Language Understanding by Generative Pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; and Sutskever, I. 2018 · 2018
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Fairness of Exposure in Rankings
Singh, A.; and Joachims, T. 2018 · 2018
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Conversational recommender system
Sun, Y.; and Zhang, Y. 2018 · 2018
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Popularity Bias in Ranking and Recommendation
Abdollahpouri, H. 2019 · 2019
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Designing Fair Ranking Schemes
Asudeh, A.; Jagadish, H.; Stoyanovich, J.; and Das, G. 2019 · 2019
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Multiagent Mechanism Design Without Money
Balseiro, S. R.; Gurkan, H.; and Sun, P. 2019 · 2019
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Koren, Y.; Bell, R.; and Volinsky, C. 2009 · 2009
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The Dynamic Pivot Mechanism
Bergemann, D.; and Välimäki, J. 2010 · 2010
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The Long Tail in Recommender Systems
Celma, Ò. 2010 · 2010
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Incentive Compatible Regression Learning
Dekel, O.; Fischer, F.; and Procaccia, A. D. 2010 · 2010
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A Contextual-Bandit Approach to Personalized News Article Recommendation
Li, L.; Chu, W.; Langford, J.; and Schapire, R. 2010 · 2010
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On the Optimal Product Line Selection Problem with Price Discrimination
Schön, C. 2010 · 2010
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Advances in Collaborative Filtering
Koren, Y.; Rendle, S.; and Bell, R. 2011 · 2011
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Ben-Porat, O.; Goren, G.; Rosenberg, I.; and Tennenholtz, M. 2019 · 2019
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Dynamic Mechanism Design: An Introduction
Bergemann, D.; and Välimäki, J. 2019 · 2019
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Intertemporal Choice
Ericson, K. M.; and Laibson, D. 2019 · 2019
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Learning to Groove with Inverse Sequence Transformations
Gillick, J.; Roberts, A.; Engel, J.; Eck, D.; and Bamman, D. 2019 · 2019
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SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
Ie, E.; Jain, V.; Wang, J.; Narvekar, S.; Agarwal, R.; Wu, R.; Cheng, H.-T.; Chandra, T.; and Boutilier, C. 2019 · 2019
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Optimal User Choice Engineering in Mobile Crowdsensing with Bounded Rational Users
Karaliopoulos, M.; Koutsopoulos, I.; and Spiliopoulos, L. 2019 · 2019
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Joint Optimization of Profit and Relevance for Recommendation Systems in E-commerce
Louca, R.; Bhattacharya, M.; Hu, D.; and Hong, L. 2019 · 2019
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Rethinking Search Engines and Recommendation Systems: A Game Theoretic Perspective
Tennenholtz, M.; and Kurland, O. 2019 · 2019
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Sampling-bias-corrected Neural Modeling for Large Corpus Item Recommendations
Yi, X.; Yang, J.; Hong, L.; Cheng, D. Z.; Heldt, L.; Kumthekar, A.; Zhao, Z.; Wei, L.; and Chi, E. 2019 · 2019
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Multistakeholder Recommendation: Survey and Research Directions
Abdollahpouri, H.; Adomavicius, G.; Burke, R.; Guy, I.; Jannach, D.; Kamishima, T.; Krasnodebski, J.; and Pizzato, L. 2020 · 2020
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Deconstructing the Filter Bubble: User Decision-making and Recommender Systems
Aridor, G.; Goncalves, D.; and Sikdar, S. 2020 · 2020
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Why We’re Polarized
Klein, E. 2020 · 2020
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Competing Bandits in Matching Markets
Liu, L. T.; Mania, H.; and Jordan, M. 2020 · 2020
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Preference Elicitation and Robust Winner Determination for Single- and Multi-winner Social Choice
Lu, T.; and Boutilier, C. 2020 · 2020
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Auditing Radicalization Pathways on YouTube
Ribeiro, M. H.; Ottoni, R.; West, R.; Almeida, V. A. F.; and Jr., W. M. 2020 · 2020
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Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations
Yang, J.; Yi, X.; Zhiyuan Cheng, D.; Hong, L.; Li, Y.; Xiaoming Wang, S.; Xu, T.; and Chi, E. H. 2020 · 2020
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iMLCA: Machine Learning-powered Iterative Combinatorial Auctions with Interval Bidding
Beyeler, M.; Brero, G.; Lubin, B.; and Seuken, S. 2021 · 2021
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Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Chien, S.; Jain, P.; Krichene, W.; Rendle, S.; Song, S.; Thakurta, A.; and Zhang, L. 2021 · 2021
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Taming Transformers for High-resolution Image Synthesis
Esser, P.; Rombach, R.; and Ommer, B. 2021 · 2021
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On YouTube’s Recommendation System
Goodrow, C. 2021 · 2021
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The Stereotyping Problem in Collaboratively Filtered Recommender Systems
Guo, W.; Krauth, K.; Jordan, M.; and Garg, N. 2021 · 2021
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Zero-shot Text-to-image Generation
Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; and Sutskever, I. 2021 · 2021
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Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay Between User and Provider Utilities
Zhan, R.; Christakopoulou, K.; Le, Y.; Ooi, J.; Mladenov, M.; Beutel, A.; Boutilier, C.; Chi, E.; and Chen, M. 2021 · 2021
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Long-term Dynamics of Fairness Intervention in Connection Recommender Systems
Akpinar, N.-J.; DiCiccio, C.; Nandy, P.; and Basu, K. 2022 · 2022
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Modeling Attrition in Recommender Systems with Departing Bandits
Ben-Porat, O.; Cohen, L.; Leqi, L.; Lipton, Z. C.; and Mansour, Y. 2022 · 2022
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Statistical Discrimination in Stable Matchings
Castera, R.; Loiseau, P.; and Pradelski, B. S. 2022 · 2022
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A Game-Theoretic Perspective on Trust in Recommendation
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Fairness in Recommender Systems
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Differentially Private Recommender System with Variational Autoencoders
Fang, L.; Du, B.; and Wu, C. 2022 · 2022
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Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors
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Fairness of Exposure in Light of Incomplete Exposure Estimation
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Modeling Content Creator Incentives on Algorithm-curated Platforms
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Supply-Side Equilibria in Recommender Systems
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Competitive Search
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Federating Recommendations using Differentially Private Prototypes
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Lamda: Language Models for Dialog Applications
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Joint Multisided Exposure Fairness for Recommendation
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Diffusion Probabilistic Modeling for Video Generation
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Fairness and Machine Learning: Limitations and Opportunities
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Automated Design of Affine Maximizer Mechanisms In Dynamic Settings
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