  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.
