DAC-GCN: A Dual Actor-Critic Graph Convolutional Network with Multi-Hop Aggregation for Enhanced Recommender Systems

Background: Recommender Systems (RSs) frequently face challenges in balancing exploration and exploitation, particularly in dynamic environments where user behaviors evolve over time. Traditional methods struggle to adapt to these complexities, limiting their effectiveness in real-world domains such as e-commerce, streaming services, and social networks.Objective: The objective of this study is to introduce DAC-GCN, a Dual Actor-Critic Graph Convolutional Network, designed to enhance recommendation accuracy, ranking quality, and adaptability to evolving user preferences. DAC-GCN merges graph-based learning with Deep Reinforcement Learning (DRL) techniques to improve both short-term and long-term user-item interactions.Methods: DAC-GCN utilizes a dual architecture with separate Graph Convolutional Networks (GCNs) for policy optimization and value estimation. It incorporates Multi-Hop Aggregation (MHA) to capture extended user-item dependencies and an attention mechanism to emphasize pivotal relationships. We evaluate DAC-GCN on benchmark datasets, including MovieLens 100K, MovieLens 1M, Amazon Subscription Boxes, Amazon Magazine Subscriptions, and Mod Cloth, using standard ranking metrics (Precision@K, Recall@K, NDCG@K, MRR@K, and Hit@K).Results: Experimental results demonstrate that DAC-GCN consistently outperforms state-of-the-art baselines, showing significant improvements in recommendation accuracy, ranking quality, and robustness to shifting user behaviors. The model's ability to capture complex user-item interactions is greatly enhanced by MHA and attention mechanisms, while the dual architecture ensures training stability.Conclusion: DAC-GCN offers a scalable, high-performance solution for modern recommender systems, effectively addressing challenges such as data sparsity and changing user preferences. By integrating graph-based methods with DRL, this study advances both the theory and practice of recommender systems and provides valuable insights for future research and practical applications.