Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912558759280640 |
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| author | Zhao, Minda |
| author_facet | Zhao, Minda |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21259 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs Zhao, Minda Information Retrieval Artificial Intelligence 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. |
| title | Breaking the Cold-Start Barrier: Reinforcement Learning with Double and Dueling DQNs |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2508.21259 |