Traditional online advertising systems for sponsored search follow a cascade paradigm with retrieval, pre-ranking,ranking, respectively. Constrained by strict requirements on online inference efficiency, it tend to be difficult to deploy useful but computationally intensive modules in the ranking stage. Moreover, ranking models currently used in the industry assume the user click only relies on the advertisements itself, which results in the ranking stage overlooking the impact of organic search results on the predicted advertisements (ads). In this work, we propose a novel framework PCDF(Parallel-Computing Distributed Framework), allowing to split the computation cost into three parts and to deploy them in the pre-module in parallel with the retrieval stage, the middle-module for ranking ads, and the post-module for re-ranking ads with external items. Our PCDF effectively reduces the overall inference latency compared with the classic framework. The whole module is end-to-end offline training and adapt for the online learning paradigm. To our knowledge, we are the first to propose an end-to-end solution for online training and deployment on complex CTR models from the system framework side.

翻译：传统赞助搜索在线广告系统遵循级联范式，包含检索、预排序和排序阶段。受在线推理效率严格要求的制约，在排序阶段部署有用但计算密集的模块往往面临困难。此外，当前工业界使用的排序模型假设用户点击仅依赖于广告本身，导致排序阶段忽略了自然搜索结果对预测广告的影响。本文提出了一种新颖的框架PCDF（并行计算分布式框架），允许将计算成本分为三个部分，并将其分别部署在与检索阶段并行的前置模块、用于广告排序的中置模块以及利用外部项目进行广告重排的后置模块中。与经典框架相比，我们的PCDF有效降低了整体推理延迟。整个模块支持端到端离线训练，并适应在线学习范式。据我们所知，我们是首个从系统框架层面提出针对复杂CTR模型的在线训练与部署的端到端解决方案。