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

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.

我们提出了一个多任务学习框架，用于早期阶段排名以捕获多个最终阶段排名组件和它们的任务关系，实现广告召回率和排名一致性的改进。通过模型整合，我们不仅可以实现成本节约，还可以在大规模工业广告排名系统中实现显著更高的点击率、转化率、总价值和更好的广告质量效果。

朝着更好的排名一致性迈进：用于早期广告排名的多任务学习框架

Towards the Better Ranking Consistency: A Multi-task Learning Framework  for Early Stage Ads Ranking

Unsupervised sentence representation learning is one of the fundamental
problems in natural language processing with various downstream applications.
Recently, contrastive learning has been widely adopted which derives
high-quality sentence representations by pulling similar semantics closer and
pushing dissimilar ones away. However, these methods fail to capture the
fine-grained ranking information among the sentences, where each sentence is
only treated as either positive or negative. In many real-world scenarios, one
needs to distinguish and rank the sentences based on their similarities to a
query sentence, e.g., very relevant, moderate relevant, less relevant,
irrelevant, etc. In this paper, we propose a novel approach, RankCSE, for
unsupervised sentence representation learning, which incorporates ranking
consistency and ranking distillation with contrastive learning into a unified
framework. In particular, we learn semantically discriminative sentence
representations by simultaneously ensuring ranking consistency between two
representations with different dropout masks, and distilling listwise ranking
knowledge from the teacher. An extensive set of experiments are conducted on
both semantic textual similarity (STS) and transfer (TR) tasks. Experimental
results demonstrate the superior performance of our approach over several
state-of-the-art baselines.

本文提出了一种新方法 RankCSE，将排名一致性和排名蒸馏与对比学习相结合，用于无监督学习句子表示，实现了语义区分的句子表示，该方法在语义文本相似性任务和转移任务中表现出优异的性能。