解决点击后转化率估计中的协变量偏移问题，提升广告系统精准度。
EGEAN: An Exposure-Guided Embedding Alignment Network for Post-Click Conversion Estimation
- 基于曝光引导的嵌入对齐网络，缓解点击与未点击数据分布差异。
- 在美团广告系统上，转化率和GMV均显著优于基线模型。
- 适合需要高精度转化预估的在线广告场景使用。
准确的点击后转化率（CVR）估计对在线广告系统至关重要。尽管因果方法已用于缓解样本选择偏差，但协变量偏移问题仍导致估计不准。本文提出暴露引导的嵌入对齐网络（EGEAN），利用点击与非点击空间中协变量分布的内在关联，降低因协变量偏移带来的估计偏差。同时，设计了一种稳态控制的参数可变双重稳健估计器，更好地处理小倾向值情况。在美团广告系统上的线上A/B测试表明，该方法在CVR和GMV指标上均显著优于基线模型。代码已开源：https://github.com/hydrogen-maker/EGEAN。
原文摘要 · Abstract (English)
Accurate post-click conversion rate (CVR) estimation is crucial for online advertising systems. Despite significant advances in causal approaches designed to address the Sample Selection Bias problem, CVR estimation still faces challenges due to Covariate Shift. Given the intrinsic connection between the distribution of covariates in the click and non-click spaces, this study proposes an Exposure-Guided Embedding Alignment Network (EGEAN) to address estimation bias caused by covariate shift. Additionally, we propose a Parameter Varying Doubly Robust Estimator with steady-state control to handle small propensities better. Online A/B tests conducted on the Meituan advertising system demonstrate that our method significantly outperforms baseline models with respect to CVR and GMV, validating its effectiveness. Code is available: https://github.com/hydrogen-maker/EGEAN.
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