用图神经网络捕捉跨平台用户兴趣变化，提升广告推荐准确率
Graph Neural Network Enhanced Sequential Recommendation Method for Cross-Platform Ad Campaign
- 构建多维行为模型，融合点击频次、使用时长等数据
- 在三平台数据上，平台B AUC达0.937，表现最优
- 适合做跨平台广告推荐系统的研发与优化
为提升跨平台广告推荐的准确性，本文分析了一种基于图神经网络（GNN）的广告推荐方法。通过多维度建模，用户行为数据（如点击频率、活跃时长）揭示兴趣演化的时序模式，广告内容（如类型、标签、时长）影响语义偏好，平台特征（如设备类型、使用场景）塑造兴趣转移环境。这些因素共同使GNN能够捕捉用户跨平台兴趣迁移的潜在路径。实验基于三个平台数据，平台B的AUC值达到0.937，表现最佳；平台A和平台C在广告标签分布不均的情况下，精度与召回率略有下降。通过调整学习率、批量大小和嵌入维度等超参数，模型在异构数据中的适应性与鲁棒性进一步提升。
原文摘要 · Abstract (English)
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.
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