用强化学习解决新用户推荐冷启动问题，无需敏感数据。
Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs
- 结合双DQN与博弈网络，从稀疏反馈中动态学习偏好
- 在电商数据集上降低冷启动用户的RMSE，优于传统方法
- 适合隐私敏感场景，无需依赖用户人口统计信息
推荐系统在面对交互历史极少的新用户时表现不佳，即冷启动问题。本文提出一种基于双深度Q网络（Double DQN）与博弈深度Q网络（Dueling DQN）的强化学习方法，通过稀疏反馈动态学习用户偏好，提升推荐精度，且不依赖敏感的人口统计信息。将该方法与矩阵分解模型结合，在大规模电商数据集上的实验表明，其性能显著优于基于流行度和主动学习的传统方法。尤其在冷用户场景下，杜林式DQN有效降低了均方根误差（RMSE），为隐私保护环境下的推荐提供了高效解决方案。
原文摘要 · Abstract (English)
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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