Know in AdVance: Linear-Complexity Forecasting of Ad Campaign Performance with Evolving User Interest
<|reference_start|>Know in AdVance: Linear-Complexity Forecasting of Ad Campaign Performance with Evolving User Interest: Real-time Bidding (RTB) advertisers wish to \textit{know in advance} the expected cost and yield of ad campaigns to avoid trial-and-error expenses. However, Campaign Performance Forecasting (CPF), a sequence modeling task involving tens of thousands of ad auctions, poses challenges of evolving user interest, auction representation, and long context, making coarse-grained and static-modeling methods sub-optimal. We propose \textit{AdVance}, a time-aware framework that integrates local auction-level and global campaign-level modeling. User preference and fatigue are disentangled using a time-positioned sequence of clicked items and a concise vector of all displayed items. Cross-attention, conditioned on the fatigue vector, captures the dynamics of user interest toward each candidate ad. Bidders compete with each other, presenting a complete graph similar to the self-attention mechanism. Hence, we employ a Transformer Encoder to compress each auction into embedding by solving auxiliary tasks. These sequential embeddings are then summarized by a conditional state space model (SSM) to comprehend long-range dependencies while maintaining global linear complexity. Considering the irregular time intervals between auctions, we make SSM's parameters dependent on the current auction embedding and the time interval. We further condition SSM's global predictions on the accumulation of local results. Extensive evaluations and ablation studies demonstrate its superiority over state-of-the-art methods. AdVance has been deployed on the Tencent Advertising platform, and A/B tests show a remarkable 4.5\% uplift in Average Revenue per User (ARPU).<|reference_end|>
@article{wang2024know,
    title={Know in AdVance: Linear-Complexity Forecasting of Ad Campaign
  Performance with Evolving User Interest},
    author={XiaoYu Wang, YongHui Guo, Hui Sheng, Peili Lv, Chi Zhou, Wei Huang,
  ShiQin Ta, Dongbo Huang, XiuJin Yang, Lan Xu, Hao Zhou, and Yusheng Ji},
    journal={arXiv preprint arXiv:2405.10681},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10681},
    primaryClass={cs.IR}
}