Paper Info
Title
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
Abstract
ABSTRACTThe 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.
Year
DOI
Venue
2021
10.1145/3404835.3463045
Research and Development in Information Retrieval
Keywords
DocType
Citations 
online advertising, conversion rate prediction, delayed feedback
Conference
0
PageRank 
References 
Authors
0.34
10
9
Name
Order
Citations
PageRank
Haoming Li100.68
Feiyang Pan201.01
Xiang Ao311921.43
Zhao Yang400.34
Min Lu5299.98
Junwei Pan6344.44
Dapeng Liu753.20
Xiao Lei8559.63
Qing He9347.02