标题： 基于图神经网络的跨平台广告活动序列推荐方法 显示英文标题

标题： Graph Neural Network Enhanced Sequential Recommendation Method for Cross-Platform Ad Campaign

摘要： 为了提高跨平台广告推荐的准确性，分析了一种基于图神经网络（GNN）的广告推荐方法。 通过多维建模，用户行为数据（例如，点击频率、活跃时长）揭示了兴趣演化的时空模式，广告内容（例如，类型、标签、时长）影响语义偏好，平台特征（例如，设备类型、使用场景）塑造了兴趣转换发生的环境。 这些因素共同使GNN能够捕捉用户兴趣在不同平台间迁移的潜在路径。 实验结果基于三个平台的数据集，平台B在AUC值上达到0.937，是最佳表现。 平台A和平台C在广告标签分布不均的情况下，精度和召回率略有下降。 通过调整学习率、批次大小和嵌入维度等超参数，进一步提高了模型在异构数据中的适应性和鲁棒性。 显示英文摘要

摘要： In order to improve the accuracy of cross-platform advertisement recommendation, a graph neural network (GNN)- based advertisement recommendation method is analyzed. Through multi-dimensional modeling, user behavior data (e.g., click frequency, active duration) reveal temporal patterns of interest evolution, ad content (e.g., type, tag, duration) influences semantic preferences, and platform features (e.g., device type, usage context) shape the environment where interest transitions occur. These factors jointly enable the GNN to capture the latent pathways of user interest migration across platforms. The experimental results are based on the datasets of three platforms, and Platform B reaches 0.937 in AUC value, which is the best performance. Platform A and Platform C showed a slight decrease in precision and recall with uneven distribution of ad labels. By adjusting the hyperparameters such as learning rate, batch size and embedding dimension, the adaptability and robustness of the model in heterogeneous data are further improved.