Abstract
Personalized recommendation remains a central challenge in modern marketing systems due to the complexity of user-product-query interactions. In this study, we propose a novel framework called DP-GCN (Deterministic Policy Graph Convolutional Network), which integrates multi-level Graph Convolutional Networks (GCNs) with Deep Deterministic Policy Gradient (DDPG) reinforcement learning to model heterogeneous information networks composed of users, products, and search queries. The proposed framework consists of three key components: (1) a graph-based embedding module to capture multi-relational structures; (2) a fusion layer that integrates dynamic and static features from users and items; and (3) a reinforcement learning layer that adaptively updates recommendation policies based on user feedback. We evaluate our model on several public benchmark datasets and a real-world dataset collected from a local e-commerce platform. Results demonstrate that DP-GCN consistently outperforms state-of-the-art baselines in AUC, Precision@K, and NDCG@K. The findings highlight the effectiveness of combining graph-based relational modeling with reinforcement learning for improving both the accuracy and adaptability of personalized recommendation systems.