Click-through rate (CTR) prediction is a crucial task in web search,
recommender systems, and online advertisement displaying. In practical
application, CTR models often serve with high-speed user-generated data
streams, whose underlying distribution rapidly changing over time. The concept
drift problem inevitably exists in those streaming data, which can lead to
performance degradation due to the timeliness issue. To ensure model freshness,
incremental learning has been widely adopted in real-world production systems.
However, it is hard for the incremental update to achieve the balance of the
CTR models between the adaptability to capture the fast-changing trends and
generalization ability to retain common knowledge. In this paper, we propose
adaptive mixture of experts (AdaMoE), a new framework to alleviate the concept
drift problem by statistical weighting policy in the data stream of CTR
prediction. The extensive offline experiments on both benchmark and a
real-world industrial dataset, as well as an online A/B testing show that our
AdaMoE significantly outperforms all incremental learning frameworks
considered.

本文提出了一种新的自适应专家混合（AdaMoE）框架，通过对 CTR 预测数据流中的统计加权策略来缓解概念漂移问题，并通过基准和实际工业数据集的大量离线实验以及在线 A / B 测试表明，AdaMoE 明显胜过所有考虑的增量学习框架。