Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction

CTR prediction is crucial in recommendation systems and online advertising platforms, where user-generated data streams that drift over time can lead to catastrophic forgetting if the model continuously adapts to new data distribution. Conventional strategies for catastrophic forgetting are challenging to deploy due to memory constraints and diverse data distributions. To address this, we propose a novel drift-aware incremental learning framework based on ensemble learning for CTR prediction, which uses explicit error-based drift detection on streaming data to strengthen well-adapted ensembles and freeze ensembles that do not match the input distribution, avoiding catastrophic interference. Our method outperforms all baselines considered in offline experiments and A/B tests.