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

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