Dividing ads ranking system into retrieval, early, and final stages is a common practice in large scale ads recommendation to balance the efficiency and accuracy. The early stage ranking often uses efficient models to generate candidates out of a set of retrieved ads. The candidates are then fed into a more computationally intensive but accurate final stage ranking system to produce the final ads recommendation. As the early and final stage ranking use different features and model architectures because of system constraints, a serious ranking consistency issue arises where the early stage has a low ads recall, i.e., top ads in the final stage are ranked low in the early stage. In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i.e. ads clicks and ads quality events) and their task relations. With our multi-task learning framework, we can not only achieve serving cost saving from the model consolidation, but also improve the ads recall and ranking consistency. In the online A/B testing, our framework achieves significantly higher click-through rate (CTR), conversion rate (CVR), total value and better ads-quality (e.g. reduced ads cross-out rate) in a large scale industrial ads ranking system.

翻译：将广告排序系统划分为召回、早期和最终阶段，是大规模广告推荐中平衡效率与准确性的常见实践。早期阶段排序通常使用高效模型从一组召回广告中生成候选集，随后将这些候选输入计算密集但精度更高的最终阶段排序系统，以产生最终广告推荐。由于系统约束导致早期与最终阶段排序采用不同的特征和模型架构，出现了严重的排序一致性问题——早期阶段的广告召回率较低，即最终阶段的优质广告在早期阶段排名靠后。为将更优质的广告从早期传递至最终阶段排序，我们提出了一种用于早期阶段排序的多任务学习框架，该框架可捕获多个最终阶段排序组件（如广告点击与广告质量事件）及其任务关联。通过这一多任务学习框架，我们不仅能通过模型整合降低服务成本，还能提升广告召回率与排序一致性。在线上A/B测试中，该框架在工业级大规模广告排序系统中显著提升了点击率（CTR）、转化率（CVR）、总价值，并改善了广告质量（例如降低了广告交叉率）。