Optimization Strategies in Consumer Choice Behavior for Personalized Recommendation Systems Based on Deep Reinforcement Learning
Zhehuan Wei, Yan Liang, Chunxi Zhang · Journal of Organizational and End User Computing · 2025
In domains such as e-commerce and media recommendations, personalized recommendation systems effectively alleviate the issue of information overload. However, existing systems still face challenges in multimodal data processing, data sparsity, and dynamic changes in user preferences. This paper proposes a Hierarchical Generative Reinforcement Learning Recommendation Optimization framework (HG-RLRO) that addresses these issues by integrating multimodal data, Generative Adversarial Networks (GAN), Inverse Reinforcement Learning (IRL), and Hierarchical Temporal Difference Learning (HTD). HG-RLRO employs a multi-agent architecture to handle textual and image data and utilizes GAN to generate simulated user behavior data to mitigate data sparsity. IRL dynamically infers user preferences across multiple time scales.