  Cascading architecture has been widely adopted in large-scale advertising
systems to balance efficiency and effectiveness. In this architecture, the
pre-ranking model is expected to be a lightweight approximation of the ranking
model, which handles more candidates with strict latency requirements. Due to
the gap in model capacity, the pre-ranking and ranking models usually generate
inconsistent ranked results, thus hurting the overall system effectiveness. The
paradigm of score alignment is proposed to regularize their raw scores to be
consistent. However, it suffers from inevitable alignment errors and error
amplification by bids when applied in online advertising. To this end, we
introduce a consistency-oriented pre-ranking framework for online advertising,
which employs a chunk-based sampling module and a plug-and-play rank alignment
module to explicitly optimize consistency of ECPM-ranked results. A $\Delta
NDCG$-based weighting mechanism is adopted to better distinguish the importance
of inter-chunk samples in optimization. Both online and offline experiments
have validated the superiority of our framework. When deployed in Taobao
display advertising system, it achieves an improvement of up to +12.3\% CTR and
+5.6\% RPM.
