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.

翻译：点击率预测是网络搜索、推荐系统和在线广告展示中的关键任务。在实际应用中，点击率模型常服务于高速生成且底层分布随时间快速变化的用户数据流。概念漂移问题不可避免地存在于这些流式数据中，由于时效性问题可能导致模型性能下降。为确保模型新鲜度，增量学习已广泛应用于实际生产系统。然而，增量更新难以使点击率模型在捕捉快速变化趋势的适应性与保留通用知识的泛化能力之间取得平衡。本文提出自适应混合专家模型（AdaMoE）——一种通过数据流统计加权策略缓解概念漂移问题的新框架。在基准数据集和真实工业数据集上的大量离线实验以及在线A/B测试表明，我们的AdaMoE方法显著优于所有被比较的增量学习框架。