用影响函数解决广告转化延迟问题，更新模型更高效准确。
Delayed Feedback Modeling with Influence Functions
- 基于影响函数估计延迟转化对模型的影响，避免全量重训练。
- 在多个基准数据集上提升预测准确率与模型适应性。
- 适合需要快速响应用户兴趣变化的在线广告系统。
在按转化成本（CPA）计费的在线广告中，精准预测转化率（CVR）至关重要。主要挑战是转化反馈延迟，即用户行为后转化可能长时间才发生，导致近期数据不完整且模型训练存在偏差。现有方法虽部分缓解此问题，但常依赖辅助模型，计算效率低且难以适应用户兴趣变化。本文提出IF-DFM（影响函数赋能的延迟反馈建模），通过估计新到达和延迟转化对模型参数的影响，实现无需全量重训练的高效更新。通过将逆海森向量积重构为优化问题，IF-DFM在可扩展性与有效性间取得良好平衡。在基准数据集上的实验表明，IF-DFM在准确率和适应性方面均优于已有方法。
原文摘要 · Abstract (English)
In online advertising under the cost-per-conversion (CPA) model, accurate conversion rate (CVR) prediction is crucial. A major challenge is delayed feedback, where conversions may occur long after user interactions, leading to incomplete recent data and biased model training. Existing solutions partially mitigate this issue but often rely on auxiliary models, making them computationally inefficient and less adaptive to user interest shifts. We propose IF-DFM, an \underline{I}nfluence \underline{F}unction-empowered for \underline{D}elayed \underline{F}eedback \underline{M}odeling which estimates the impact of newly arrived and delayed conversions on model parameters, enabling efficient updates without full retraining. By reformulating the inverse Hessian-vector product as an optimization problem, IF-DFM achieves a favorable trade-off between scalability and effectiveness. Experiments on benchmark datasets show that IF-DFM outperforms prior methods in both accuracy and adaptability.
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