Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data observed by the CTR model typically arrives as a stream. Streaming data has the characteristic that the underlying distribution drifts over time and may recur. This can lead to catastrophic forgetting if the model simply adapts to new data distribution all the time. Also, it's inefficient to relearn distribution that has been occurred. Due to memory constraints and diversity of data distributions in large-scale industrial applications, conventional strategies for catastrophic forgetting such as replay, parameter isolation, and knowledge distillation are difficult to be deployed. In this work, we design a novel drift-aware incremental learning framework based on ensemble learning to address catastrophic forgetting in CTR prediction. With explicit error-based drift detection on streaming data, the framework further strengthens well-adapted ensembles and freezes ensembles that do not match the input distribution avoiding catastrophic interference. Both evaluations on offline experiments and A/B test shows that our method outperforms all baselines considered.

翻译：点击率（CTR）预测在推荐系统和在线广告平台中具有重要意义。在工业场景中，CTR模型观测到的用户生成数据通常以流式形式到达。流式数据具有潜在分布随时间漂移且可能重复出现的特性。若模型始终简单地适应新数据分布，将导致灾难性遗忘。同时，重新学习已出现过的分布效率低下。在大规模工业应用中，受内存限制和数据分布多样性的影响，回放、参数隔离和知识蒸馏等传统灾难性遗忘应对策略难以部署。本研究基于集成学习设计了一种新颖的漂移感知增量学习框架，以解决CTR预测中的灾难性遗忘问题。通过对流式数据进行显式的基于误差的漂移检测，该框架进一步强化已充分适配的集成模块，并冻结与输入分布不匹配的集成模块，从而避免灾难性干扰。离线实验和A/B测试的评估均表明，我们的方法优于所考虑的所有基线模型。