Personalizing Fairness: Adaptive RL with User Diversity Preference for Recommender Systems

Workshop Paper · August 2025 · 1 min read

We propose an adaptive RL approach for recommender systems that personalizes fairness based on user diversity preferences.

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Personalizing Fairness: Adaptive RL with User Diversity Preference for Recommender Systems

We propose an adaptive reinforcement learning approach for recommender systems that personalizes fairness constraints based on individual user diversity preferences, balancing recommendation quality with equitable exposure.