Reinforcement Learning-Based Recommender Systems Enhanced with Graph Neural Networks

Graph Neural Networks (GNNs) have emerged as powerful tools in recommender systems, enabling the modeling of complex user-item interactions by leveraging graph-structured representations. However, conventional GNN-based recommendation models often struggle to adapt to dynamically evolving user preferences and newly introduced items, as they predominantly rely on static supervised learning frameworks. These limitations hinder their ability to provide accurate and personalized recommendations over time, particularly in scenarios where user behavior and item availability change frequently. To address this challenge, we propose a novel recommender system that integrates GNNs with Reinforcement Learning (RL), combining the structural modeling capabilities of GNNs with the sequential decision-making strengths of RL. Our approach enables continuous learning and adaptation to shifting user preferences while optimizing long-term user engagement. Key innovations include an attention-based mechanism to enhance state representations, residual connections in the Actor network to stabilize training, and a refined reward function in the Critic network to improve alignment with user satisfaction and engagement metrics. Through extensive experiments on real-world datasets, we demonstrate that our method significantly outperforms traditional GNN-based recommenders in both recommendation accuracy and adaptability. Our findings highlight the potential of integrating GNNs with RL for more effective and dynamic recommendation strategies, paving the way for next-generation personalized recommendation systems that can evolve with users over time.