Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs

Minda Zhao

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arXiv · 2508.21259

Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs

Minda Zhao

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Abstract

Recommender systems struggle to provide accurate suggestions to new users with limited interaction history, a challenge known as the cold-user problem. This paper proposes a reinforcement learning approach using Double and Dueling Deep Q-Networks (DQN) to dynamically learn user preferences from sparse feedback, enhancing recommendation accuracy without relying on sensitive demographic data. By integrating these advanced DQN variants with a matrix factorization model, we achieve superior performance on a large e-commerce dataset compared to traditional methods like popularity-based and active learning strategies. Experimental results show that our method, particularly Dueling DQN, reduces Root Mean Square Error (RMSE) for cold users, offering an effective solution for privacy-constrained environments.

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Minda Zhao. 2025-08-28. Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs. https://arxiv.org/abs/2508.21259

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