HT-GNN: Hyper-Temporal Graph Neural Network for Customer Lifetime Value Prediction in Baidu Ads
This work improves LTV prediction for advertising platforms like Baidu Ads, enabling better bidding and budget allocation, though it appears incremental as it builds on existing graph neural network and transformer techniques.
The paper tackled the problem of predicting customer lifetime value (LTV) in news feed advertising by addressing challenges like demographic heterogeneity and dynamic behavioral sequences, resulting in a model that consistently outperformed state-of-the-art methods across all metrics and horizons on a dataset of 15 million users.
Lifetime value (LTV) prediction is crucial for news feed advertising, enabling platforms to optimize bidding and budget allocation for long-term revenue growth. However, it faces two major challenges: (1) demographic-based targeting creates segment-specific LTV distributions with large value variations across user groups; and (2) dynamic marketing strategies generate irregular behavioral sequences where engagement patterns evolve rapidly. We propose a Hyper-Temporal Graph Neural Network (HT-GNN), which jointly models demographic heterogeneity and temporal dynamics through three key components: (i) a hypergraph-supervised module capturing inter-segment relationships; (ii) a transformer-based temporal encoder with adaptive weighting; and (iii) a task-adaptive mixture-of-experts with dynamic prediction towers for multi-horizon LTV forecasting. Experiments on \textit{Baidu Ads} with 15 million users demonstrate that HT-GNN consistently outperforms state-of-the-art methods across all metrics and prediction horizons.