Addressing Delayed Feedback in Conversion Rate Prediction: A Domain Adaptation Approach

Leisheng Yu, Yanxiao Cai, Lucas Chen, Minxing Zhang, Wei-Yen Day, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu

Abstract: In the rapidly evolving online display advertising market, conversion rate (CVR) prediction models are typically updated daily using datasets enriched with recent conversion logs. However, a significant challenge is the time gap, often spanning days or weeks, between ad clicks and conversions. This issue, known as delayed feedback, results in false negatives in training data, creating a dilemma between label accuracy and data freshness. Existing methods for mitigating delayed feedback are limited, due to strong underlying assumptions, insufficient use of recent data without observed conversions, or implicit control over false negatives. To address this, we propose a simple framework that redefines CVR prediction under delayed feedback as an unsupervised domain adaptation (UDA) problem. Our method learns from fresh data while minimizing the impact of inaccurate labels, by integrating existing click-through rate (CTR) or CVR models with UDA algorithms. A customized pretraining step is also incorporated to effectively utilize recent observed conversions. Comprehensive experiments on three datasets showcase the proposed method's superiority over state-of-the-art approaches and its potential to benefit from advancements in CTR modeling. The code is available at https://github.com/ThunderbornSakana/DelayAdapter.