mlsanityData to 14 Oct 2026

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

arXiv 2508.21259 ↗First posted 28 Aug 2025cs.IR cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 29 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

9 of 29 references.

References · 9

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  3. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  4. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  5. 2018Deep Reinforcement Learning for Page-wise Recommendationscited by 100
  6. 2013Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approachcited by 22
  7. 2021Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learningcited by 14
  8. 2018Reinforcement Learning based Recommender System using Biclustering Techniquecited by 12
  9. 2018Handling Cold-Start Collaborative Filtering with Reinforcement Learningcited by 2

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  1. 2026Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
  2. 2025Predicting E-commerce Purchase Behavior using a DQN-Inspired Deep Learning Model for enhanced adaptability
  3. 2025Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning
  4. 2020Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation
  5. 2021Compressive Features in Offline Reinforcement Learning for Recommender Systems
  6. 2021Value Penalized Q-Learning for Recommender Systems
  7. 2024DQRM: Deep Quantized Recommendation Models
  8. 2025β-DQN: Improving Deep Q-Learning By Evolving the Behavior