The delayed feedback problem is one of the most pressing challenges in
predicting the conversion rate since users' conversions are always delayed in
online commercial systems. Although new data are beneficial for continuous
training, without complete feedback information, i.e., conversion labels,
training algorithms may suffer from overwhelming fake negatives. Existing
methods tend to use multitask learning or design data pipelines to solve the
delayed feedback problem. However, these methods have a trade-off between data
freshness and label accuracy. In this paper, we propose Delayed Feedback
Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages,
i.e., preparing a data pipeline, building a dynamic graph, and training a CVR
prediction model. In the model training, we propose a novel graph convolutional
method named HLGCN, which leverages both high-pass and low-pass filters to deal
with conversion and non-conversion relationships. The proposed method achieves
both data freshness and label accuracy. We conduct extensive experiments on
three industry datasets, which validate the consistent superiority of our
method.

在本研究中，我们提出了一种名为 DGDFEM 的延迟反馈建模方法，它使用动态图神经网络来解决预测转化率中的延迟反馈问题，并在模型训练中提出了一种名为 HLGCN 的新型图卷积方法，实现了数据新鲜度和标签准确性的平衡。我们在三个行业数据集上进行了大量实验证明了我们方法的一致优越性。

鲜度还是准确度，何不兼得？通过动态图神经网络解决延迟反馈

Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via  Dynamic Graph Neural Networks

The delayed feedback problem is one of the imperative challenges in online
advertising, which is caused by the highly diversified feedback delay of a
conversion varying from a few minutes to several days. It is hard to design an
appropriate online learning system under these non-identical delay for
different types of ads and users. In this paper, we propose to tackle the
delayed feedback problem in online advertising by "Following the Prophet" (FTP
for short). The key insight is that, if the feedback came instantly for all the
logged samples, we could get a model without delayed feedback, namely the
"prophet". Although the prophet cannot be obtained during online learning, we
show that we could predict the prophet's predictions by an aggregation policy
on top of a set of multi-task predictions, where each task captures the
feedback patterns of different periods. We propose the objective and
optimization approach for the policy, and use the logged data to imitate the
prophet. Extensive experiments on three real-world advertising datasets show
that our method outperforms the previous state-of-the-art baselines.

本文提出了一种基于 FTP 的在线广告延迟反馈问题的解决方案，通过一种多任务预测和聚合策略，从不同时间段的反馈样本中预测 “先知” 预测结果，进行在线学习。实验表明，该方法优于之前的基准线。

跟随先知：在滞后反馈下精准预测网站转化率

Follow the Prophet: Accurate Online Conversion Rate Prediction in the  Face of Delayed Feedback

Conversion rate (CVR) prediction is one of the most critical tasks for
digital display advertising. Commercial systems often require to update models
in an online learning manner to catch up with the evolving data distribution.
However, conversions usually do not happen immediately after a user click. This
may result in inaccurate labeling, which is called delayed feedback problem. In
previous studies, delayed feedback problem is handled either by waiting
positive label for a long period of time, or by consuming the negative sample
on its arrival and then insert a positive duplicate when a conversion happens
later. Indeed, there is a trade-off between waiting for more accurate labels
and utilizing fresh data, which is not considered in existing works. To strike
a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback
Model (ES-DFM), which models the relationship between the observed conversion
distribution and the true conversion distribution. Then we optimize the
expectation of true conversion distribution via importance sampling under the
elapsed-time sampling distribution. We further estimate the importance weight
for each instance, which is used as the weight of loss function in CVR
prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive
experiments on a public data and a private industrial dataset. Experimental
results confirm that our method consistently outperforms the previous
state-of-the-art results.

本文提出了一种基于时间采样的延迟反馈模型（ES-DFM），利用重要性采样方法优化真实转化分布的期望，并基于此为 CVR 预测的 loss function 赋权，以解决数字广告中的转化率预测问题。