The experiment environment.

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.个性化推荐始终是现代营销系统的核心挑战，究其根源在于用户、商品与搜索查询之间的交互关系具有高度复杂性。本研究提出一种名为确定性策略图卷积网络（Deterministic Policy Graph Convolutional Network，简称DP-GCN）的新型框架，该框架将多层图卷积网络（Graph Convolutional Networks, GCNs）与深度确定性策略梯度（Deep Deterministic Policy Gradient, DDPG）强化学习相结合，用于对由用户、商品及搜索查询构成的异质信息网络进行建模。所提框架包含三大核心组件：(1) 基于图的嵌入模块，用于捕获多关系结构特征；(2) 融合层，用于整合用户与商品的动态及静态特征；(3) 强化学习层，可基于用户反馈自适应更新推荐策略。本研究在多个公开基准数据集以及从本地电商平台采集的真实数据集上对所提模型进行了评估，结果表明DP-GCN在AUC（受试者工作特征曲线下面积，Area Under Curve）、Precision@K（精确率@K）以及NDCG@K（归一化折损累计增益，Normalized Discounted Cumulative Gain@K）三项指标上均持续优于当前最优基线模型。研究结果证实，将基于图的关系建模与强化学习相结合，可有效提升个性化推荐系统的准确率与自适应能力，凸显了该融合方案的应用价值。