Customer-centric marketing campaigns generate a large portion of e-commerce website traffic for Walmart. As the scale of customer data grows larger, expanding the marketing audience to reach more customers is becoming more critical for e-commerce companies to drive business growth and bring more value to customers. In this paper, we present a scalable and efficient system to expand targeted audience of marketing campaigns, which can handle hundreds of millions of customers. We use a deep learning based embedding model to represent customers and an approximate nearest neighbor search method to quickly find lookalike customers of interest. The model can deal with various business interests by constructing interpretable and meaningful customer similarity metrics. We conduct extensive experiments to demonstrate the great performance of our system and customer embedding model.

翻译：以客户为中心的营销活动为沃尔玛的电子商务网站带来了大量流量。随着客户数据规模不断扩大，扩大营销受众以触达更多客户，对于电子商务公司推动业务增长、为客户创造更多价值而言愈发关键。本文提出了一种可扩展且高效的系统，用于扩大营销活动的目标受众，该系统可处理数亿级别的客户数据。我们采用基于深度学习的嵌入模型来表示客户，并利用近似最近邻搜索方法快速找到感兴趣的相似客户。该模型通过构建可解释且有意义的客户相似性度量指标，能够处理多种业务需求。我们进行了大量实验，证明了该系统及客户嵌入模型的卓越性能。