Conversion rate prediction is critical to many online applications such as digital display advertising. To capture dynamic data distribution, industrial systems often require retraining models on recent data daily or weekly. However, the delay of conversion behavior usually leads to incorrect labeling, which is called delayed feedback problem. Existing work may fail to introduce the correct information about false negative samples due to data sparsity and dynamic data distribution. To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples. Firstly, we theoretically prove that the label-corrected loss is an unbiased estimate of the oracle loss using true labels. Then, as there are no ready training data for label correction, counterfactual labeling is used to construct artificial training data. Furthermore, since counterfactual labeling utilizes only partial training data, we design an embedding-based alternative training method to enhance performance. Comparative experiments on both public and private datasets and detailed analyses show that our proposed approach effectively alleviates the delayed feedback problem and consistently outperforms the previous state-of-the-art methods.

翻译：转化率预测对于数字展示广告等众多在线应用至关重要。为捕捉动态数据分布，工业系统通常需要每日或每周基于最新数据重新训练模型。然而，转化行为的延迟常导致错误标注，即所谓的延迟反馈问题。现有方法因数据稀疏性和动态数据分布，难以准确引入假阴性样本的正确信息。为直接引入正确的反馈标签信息，我们提出一种无偏延迟反馈标签修正框架（ULC），该框架利用辅助模型对观测到的负反馈样本进行标签修正。首先，我们从理论上证明了标签修正后的损失是对使用真实标签的 oracle 损失的无偏估计。然后，由于缺乏现成的标签修正训练数据，我们采用反事实标注构建人工训练数据。此外，鉴于反事实标注仅利用部分训练数据，我们设计了一种基于嵌入的交替训练方法以提升性能。在公开和私有数据集上的对比实验及详细分析表明，所提方法有效缓解了延迟反馈问题，并持续优于此前的最优方法。