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September 10, 2025

Introduction to the Special Issue on Deep Reinforcement Learning for Recommender Systems

Key Points

Deep reinforcement learning enhances the adaptability of recommender systems, improving user experience and satisfaction.
Key challenges include designing effective reward functions and maintaining sample efficiency in data-sparse environments.
The collection showcases research tackling complex action spaces and training dynamics to advance recommender system performance.
Recent advancements in reinforcement learning contribute to creating more robust and personalized recommendation systems.

Abstract

Deep Reinforcement Learning (DRL) has garnered significant attention as a promising approach for developing intelligent and adaptive recommender systems. This paradigm is especially well-suited for recommendation scenarios characterized by dynamic user environments, temporally delayed feedback signals, and continuously shifting user preferences. However, deploying DRL within recommendation scenarios introduces a range of intricate challenges. These include the design of meaningful and task-aligned reward functions, effective navigation of vast and complex action spaces, and the need to maintain sample efficiency in data-sparse environments. Ensuring robust and stable training dynamics adds further difficulty. This special issue brings together a diverse collection of cutting-edge research that addresses these pressing challenges, showcasing advances that move the field toward more adaptive, robust, and personalized recommendation systems grounded in reinforcement learning.

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