Optimization Strategies in Consumer Choice Behavior for Personalized Recommendation Systems Based on Deep Reinforcement Learning

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Author(s): Zhehuan Wei (School of Economics and Management, China University of Geoscience, Wuhan, China), Liang Yan (School of Economics and Management, China University of Geoscience, Wuhan, China)and Chunxi Zhang (The Palatine Centre, Durham University, UK)
Copyright: 2025
Volume: 37
Issue: 1
Pages: 35
Source title: Journal of Organizational and End User Computing (JOEUC)
Editor(s)-in-Chief: Sangbing (Jason) Tsai (International Engineering and Technology Institute (IETI), Hong Kong)and Wei Liu (Qingdao University, China)
DOI: 10.4018/JOEUC.368009

Keywords: Computer Science & IT / End-User Computing / Engineering Science Reference / Human-Computer Interaction

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Abstract

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.

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Optimization Strategies in Consumer Choice Behavior for Personalized Recommendation Systems Based on Deep Reinforcement Learning

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