Kavli Affiliate: Feng Yuan
| First 5 Authors: Yanjun Dai, Haoyang Feng, Yuan Gao, ,
| Summary:
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.
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