In large e-commerce platforms, search systems are typically composed of a
series of modules, including recall, pre-ranking, and ranking phases. The
pre-ranking phase, serving as a lightweight module, is crucial for filtering
out the bulk of products in advance for the downstream ranking module.
Industrial efforts on optimizing the pre-ranking model have predominantly
focused on enhancing ranking consistency, model structure, and generalization
towards long-tail items. Beyond these optimizations, meeting the system
performance requirements presents a significant challenge. Contrasting with
existing industry works, we propose a novel method: a Generalizable and
RAnk-ConsistEnt Pre-Ranking Model (GRACE), which achieves: 1) Ranking
consistency by introducing multiple binary classification tasks that predict
whether a product is within the top-k results as estimated by the ranking
model, which facilitates the addition of learning objectives on common
point-wise ranking models; 2) Generalizability through contrastive learning of
representation for all products by pre-training on a subset of ranking product
embeddings; 3) Ease of implementation in feature construction and online
deployment. Our extensive experiments demonstrate significant improvements in
both offline metrics and online A/B test: a 0.75% increase in AUC and a 1.28%
increase in CVR.

在大型电子商务平台中，搜索系统通常由一系列模块组成，其中包括召回、预排序和排序阶段。我们提出了一种新颖的方法：一种具有一般化和秩次一致性的预排序模型（GRACE），通过引入多个二分类任务来预测产品是否在排名模型估计的前 k 个结果中，从而实现排名的一致性，并通过预训练对所有产品的对比学习来实现一般化。实验证明，在离线度量和在线 A/B 测试中都取得了显著的改进：AUC 增加了 0.75％，CVR 增加了 1.28％。

优化电子商务搜索：朝向可泛化和排名一致的预排序模型

Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent  Pre-Ranking Model

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.

提出了一种面向在线广告的一致性预排名框架，包括使用基于块的采样模块和即插即用的等级对齐模块，显式地优化 ECPM 排名结果的一致性。