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。