针对广告转化延迟差异，提出个性化插值方法提升预测精度与效率。
Personalized Interpolation: Achieving Efficient Conversion Estimation with Flexible Optimization Windows
- 基于可调优化窗口的个性化插值，适应不同广告主延迟特性。
- 实测在真实广告系统中显著提升预测准确率与计算效率。
- 适合大规模广告系统中需要灵活转化建模的场景。
优化转化率对现代在线广告系统至关重要，有助于向用户精准推送商品并推动业务成果。然而，由于用户互动（如曝光或点击）与实际转化之间存在可变的时间延迟，准确预测转化事件仍具挑战性。这些延迟在不同广告主和产品间差异显著，需采用针对特定转化行为的灵活优化窗口。为此，我们提出一种新的个性化插值方法，将现有固定转化窗口模型扩展为支持广告主定制的灵活优化窗口。该方法可在不增加系统复杂度的前提下，实现对多样化延迟分布的高精度转化估计。通过在真实广告转化模型上的大规模实验验证，结果表明该方法在预测精度和效率上均优于现有方案。本研究展示了个性化插值在提升转化优化能力、支持更广泛广告策略方面的潜力。
原文摘要 · Abstract (English)
Optimizing conversions is crucial in modern online advertising systems, enabling advertisers to deliver relevant products to users and drive business outcomes. However, accurately predicting conversion events remains challenging due to variable time delays between user interactions (e.g., impressions or clicks) and the actual conversions. These delays vary substantially across advertisers and products, necessitating flexible optimization windows tailored to specific conversion behaviors. To address this, we propose a novel \textit{Personalized Interpolation} method that extends existing models based on fixed conversion windows to support flexible advertiser-specific optimization windows. Our method enables accurate conversion estimation across diverse delay distributions without increasing system complexity. We evaluate the effectiveness of the proposed approach through extensive experiments using a real-world ads conversion model. Our results show that this method achieves both high prediction accuracy and improved efficiency compared to existing solutions. This study demonstrates the potential of our Personalized Interpolation method to improve conversion optimization and support a wider range of advertising strategies in large-scale online advertising systems.
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