EGEAN: An Exposure-Guided Embedding Alignment Network for Post-Click Conversion Estimation

• URL: http://arxiv.org/abs/2412.06852v1
• Date: Sun, 08 Dec 2024 10:17:02 GMT
• Title: EGEAN: An Exposure-Guided Embedding Alignment Network for Post-Click Conversion Estimation
• Authors: Huajian Feng, Guoxiao Zhang, Yadong Zhang, Yi We, Qiang Liu,
• Abstract summary: Post-click conversion rate (CVR) estimation is crucial for online advertising systems. Despite advances in causal approaches, CVR estimation still faces challenges due to Covariate Shift. This study proposes an Exposure-Guided Embedding Alignment Network (EGEAN) to address this problem.
• Score: 6.178133899988549
• License:
• Abstract: 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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