新鲜度还是准确性，为何不能兼得？通过动态图神经网络解决延迟反馈问题
Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks
AI总结：
针对转化率预测中的延迟反馈导致数据新鲜度与标签准确性难以兼顾的问题，提出包含动态图构建与 HLGCN 的 DGDFEM 方法，并在三个工业数据集上验证其优势。
摘要：
延迟反馈问题是转化率预测中最紧迫的挑战之一，因为在在线商业系统中，用户的转化总是存在延迟。尽管新数据有利于持续训练，但如果没有完整的反馈信息（即转化标签），训练算法可能会受到大量假负样本的困扰。现有方法倾向于使用多任务学习或设计数据流水线来解决延迟反馈问题。然而，这些方法在数据新鲜度和标签准确性之间存在权衡。本文提出了基于动态图神经网络的延迟反馈建模方法 Delayed Feedback Modeling by Dynamic Graph Neural Network（DGDFEM）。该方法包括三个阶段，即准备数据流水线、构建动态图以及训练 CVR 预测模型。在模型训练中，我们提出了一种名为 HLGCN 的新型图卷积方法，该方法同时利用高通滤波器和低通滤波器来处理转化与未转化关系。所提方法同时实现了数据新鲜度和标签准确性。我们在三个工业数据集上进行了大量实验，结果验证了该方法始终如一的优越性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
