用多任务学习提升广告中罕见转化的预测准确率
Practical Multi-Task Learning for Rare Conversions in Ad Tech
- 共享特征表示，不同转化类型用独立任务塔
- 离线AUC提升0.69%，线上每行动成本降低2%
- 适合处理广告点击率预测中的稀有事件
我们提出一种多任务学习（MTL）方法，用于提升在线广告中罕见转化事件（如低于1%）的预测效果。根据历史数据将转化分为“稀有”和“常见”两类，模型在所有信号上学习共享表示，并为每类设置独立的任务塔进行专门化学习。该方法已在生产环境完成测试并全量部署，展现出稳定的性能提升：离线评估中AUC提升0.69%，线上关键指标成本每行动（Cost per Action）降低2%。
原文摘要 · Abstract (English)
We present a Multi-Task Learning (MTL) approach for improving predictions for rare (e.g., <1%) conversion events in online advertising. The conversions are classified into "rare" or "frequent" types based on historical statistics. The model learns shared representations across all signals while specializing through separate task towers for each type. The approach was tested and fully deployed to production, demonstrating consistent improvements in both offline (0.69% AUC lift) and online KPI performance metric (2% Cost per Action reduction).
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