Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via
  Dynamic Graph Neural Networks
Xiaolin Zheng Zhongyu Wang Chaochao Chen Feng Zhu Jiashu Qian
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.