重新思考大规模预排序系统：整链路跨域模型
Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models
AI总结：
针对预排序系统忽略整链路数据依赖导致样本选择偏差的问题，提出整链路跨域模型ECM及细粒度结构ECMM，利用全级联阶段样本并引入L0正则化路由，在真实流量日志上超越SOTA且保持时间消耗可接受。
摘要：
推荐系统和在线广告等工业系统已广泛采用多阶段架构，这些架构被划分为多个级联模块，包括匹配、预排序、排序和重排序。作为匹配和排序之间的关键桥梁，现有的预排序方法主要由于忽略了整链路数据依赖性而遭受样本选择偏差（SSB）问题，导致性能次优。在本文中，我们从整个样本空间的角度重新思考预排序系统，并提出了整链路跨域模型（ECM），该模型利用整个级联阶段的样本来有效缓解SSB问题。此外，我们设计了一个名为ECMM的细粒度神经结构，以进一步提高预排序准确性。具体来说，我们提出了一个跨域多塔神经网络来全面预测每个阶段的结果，并引入了带有$L0$正则化的子网络路由策略以降低计算成本。在真实世界的大规模流量日志上的评估表明，我们的预排序模型在时间消耗保持在可接受水平的同时优于SOTA方法，实现了效率与效果之间更好的权衡。
英文摘要：
Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking and re-ranking. As a critical bridge between matching and ranking, existing pre-ranking approaches mainly endure sample selection bias (SSB) problem owing to ignoring the entire-chain data dependence, resulting in sub-optimal performances. In this paper, we rethink pre-ranking system from the perspective of the entire sample space, and propose Entire-chain Cross-domain Models (ECM), which leverage samples from the whole cascaded stages to effectively alleviate SSB problem. Besides, we design a fine-grained neural structure named ECMM to further improve the pre-ranking accuracy. Specifically, we propose a cross-domain multi-tower neural network to comprehensively predict for each stage result, and introduce the sub-networking routing strategy with $L0$ regularization to reduce computational costs. Evaluations on real-world large-scale traffic logs demonstrate that our pre-ranking models outperform SOTA methods while time consumption is maintained within an acceptable level, which achieves better trade-off between efficiency and effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
