2018
NIPS
NeurIPS 2018
A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers
Abstract
We introduce a game-theoretic approach to the study of recommendation systems with strategic content providers. Such systems should be fair and stable. Showing that traditional approaches fail to satisfy these requirements, we propose the Shapley mediator. We show that the Shapley mediator satisfies the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties.
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Interdisciplinary Bridge
— Data Science & Analytics and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— shapley mediator
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Hot Topic Early Bird
— recommendation system
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio