Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network

While online advertising is highly dependent on implicit interaction networks of anonymous users for engagement inference, and for the selection and optimization of delivery strategies, existing graph models seldom can capture the multi-scale temporal, semantic and higher-order dependency features of these interaction networks, thus it's hard to describe the complicated patterns of the anonymous behavior. In this paper, we propose Decoupled Temporal-Hierarchical Graph Neural Network (DTH-GNN), which achieves three main contributions. Above all, we introduce temporal edge decomposition, which divides each interaction into three types of channels: short-term burst, diurnal cycle and long-range memory, and conducts feature extraction using the convolution kernel of parallel dilated residuals; Furthermore, our model builds a hierarchical heterogeneous aggregation, where user-user, user-advertisement, advertisement-advertisement subgraphs are combined through the meta-path conditional Transformer encoder, where the noise structure is dynamically tamped down via the synergy of cross-channel self-attention and gating relationship selector. Thirdly, the contrast regularity of feedback perception is formulated, the consistency of various time slices is maximized, the entropy of control exposure information with dual-view target is maximized, the global prototype of dual-momentum queue distillation is presented, and the strategy gradient layer with light weight is combined with delaying transformation signal to fine-tune the node representation for benefit-oriented. The AUC of DTH-GNN improved by 8.2% and the logarithmic loss improved by 5.7% in comparison with the best baseline model.

@article{dai2025_2506.13787,
  title={ Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network },
  author={ Yanjun Dai and Haoyang Feng and Yuan Gao },
  journal={arXiv preprint arXiv:2506.13787},
  year={ 2025 }
}