In this work we introduce an incremental learning framework for
Click-Through-Rate (CTR) prediction and demonstrate its effectiveness for
Taboola's massive-scale recommendation service. Our approach enables rapid
capture of emerging trends through warm-starting from previously deployed
models and fine tuning on "fresh" data only. Past knowledge is maintained via a
teacher-student paradigm, where the teacher acts as a distillation technique,
mitigating the catastrophic forgetting phenomenon. Our incremental learning
framework enables significantly faster training and deployment cycles (x12
speedup). We demonstrate a consistent Revenue Per Mille (RPM) lift over
multiple traffic segments and a significant CTR increase on newly introduced
items.

本研究提出了一种增量学习框架，用于 Click-Through-Rate (CTR) 预测，并展示了其在 Taboola 庞大规模推荐服务中的有效性。通过从以前部署的模型进行暖启动和仅在 “新鲜” 的数据上微调，我们的方法可以快速捕捉新兴趋势，并通过教师 - 学生范式来保留过去的知识。我们的增量学习框架可以实现显着更快的训练和部署周期（12 倍加速）。我们在多个流量段上证明了一致的 Revenue Per Mille (RPM) 增长和新引入项目的 CTR 显著增加。