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Recommending to Strategic Users
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Recommendation systems are pervasive in the digital economy. An important assumption in many deployed systems is that user consumption reflects user preferences in a static sense: users consume the content they like with no other considerations in mind. However, as we document in a large-scale online survey, users do choose content strategically to influence the types of content they get recommended in the future. We model this user behavior as a two-stage noisy signalling game between the recommendation system and users: the recommendation system initially commits to a recommendation policy, presents content to the users during a cold start phase which the users choose to strategically consume in order to affect the types of content they will be recommended in a recommendation phase. We show that in equilibrium, users engage in behaviors that accentuate their differences to users of different preference profiles. In addition, (statistical) minorities out of fear of losing their minority content exposition may not consume content that is liked by mainstream users. We next propose three interventions that may improve recommendation quality (both on average and for minorities) when taking into account strategic consumption: (1) Adopting a recommendation system policy that uses preferences from a prior, (2) Communicating to users that universally liked ("mainstream") content will not be used as basis of recommendation, and (3) Serving content that is personalized-enough yet expected to be liked in the beginning. Finally, we describe a methodology to inform applied theory modeling with survey results.
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Cited by 5 Pith papers
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Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation Content
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The price of item fairness in recommendations falls as user preferences become more diverse, but rises sharply for users whose preferences are misestimated.
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Desirable Effort Fairness and Optimality Trade-offs in Strategic Learning
Constraining a strategic classifier to keep desirable-effort incentives fair between two groups costs the principal an explicit accuracy or welfare loss bounded by the fairness tolerance beta.
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Reactive Users vs. Social Recommender Systems: Managing Opinion Drifts with Adaptive Policies
An adaptive 'pull back after drift' content consumption policy can prevent recommendation-induced opinion drift and, when users value opinion preservation, can yield higher expected utility than a fixed engagement policy.
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Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causality
A taxonomy and literature review of strategic classification and performative prediction, organized into robustness, fairness, and improvement/causality perspectives.
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