Impatient Bandits: Optimizing for the Long-Term Without Delay

The paper presents a novel approach to optimizing recommender systems for long-term user satisfaction by addressing the challenge of delayed rewards in a bandit framework. It introduces a predictive model that integrates historical data to estimate delayed rewards and a bandit algorithm that leverages this model to identify content that promotes sustained user engagement. The proposed method shows significant improvements over traditional short-term and delayed reward optimization strategies in a large-scale podcast recommendation system, highlighting its practical applicability for enhancing user experience in real-world applications.