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Traditional online advertising systems for sponsored search follow a cascadeparadigm with retrieval, pre-ranking,ranking, respectively. Constrained bystrict requirements on online inference efficiency, it tend to be difficult todeploy useful but computationally intensive modules in the ranking stage.Moreover, ranking models currently used in the industry assume the user clickonly relies on the advertisements itself, which results in the ranking stageoverlooking the impact of organic search results on the predictedadvertisements (ads). In this work, we propose a novel frameworkPCDF(Parallel-Computing Distributed Framework), allowing to split thecomputation cost into three parts and to deploy them in the pre-module inparallel with the retrieval stage, the middle-module for ranking ads, and thepost-module for re-ranking ads with external items. Our PCDF effectivelyreduces the overall inference latency compared with the classic framework. Thewhole module is end-to-end offline training and adapt for the online learningparadigm. To our knowledge, we are the first to propose an end-to-end solutionfor online training and deployment on complex CTR models from the systemframework side.

PCDF: A Parallel-Computing Distributed Framework for Sponsored Search Advertising Serving