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.

翻译：级联架构已在大规模广告系统中被广泛采用，以平衡效率与效果。在该架构中，预排序模型被设计为排序模型的轻量级近似，需处理更多候选且满足严格的延迟要求。由于模型容量差异，预排序模型与排序模型通常生成不一致的排序结果，从而损害系统整体效果。为此，学界提出了分数对齐范式以约束两者的原始分数趋于一致。然而，该方法面临难以避免的对齐误差，且该误差在在线广告场景中会通过竞价产生放大效应。针对此问题，我们提出一种面向一致性的在线广告预排序框架，通过引入基于分块的采样模块与即插即用的排序对齐模块，显式优化ECPM排序结果的一致性。采用基于ΔNDCG的加权机制，更好区分优化过程中分块间样本的重要性。在线与离线实验均验证了本框架的优越性。在淘宝展示广告系统部署后，CTR提升最高达12.3%，RPM提升最高达5.6%。