With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies. The critical challenges faced by a modern industrial-level bidding system include: 1. the catalog is enormous, and the relevant bidding features are of high sparsity; 2. the large volume of bidding requests induces significant computation burden to both the offline and online serving. Leveraging extraneous user-item information proves essential to mitigate the sparsity issue, for which we exploit the natural language signals from the users' query and the contextual knowledge from the products. In particular, we extract the vector representations of ads via the Transformer model and leverage their geometric relation to building collaborative bidding predictions via clustering. The two-step procedure also significantly reduces the computation stress of bid evaluation and optimization. In this paper, we introduce the end-to-end structure of the bidding system for search engine marketing for Walmart e-commerce, which successfully handles tens of millions of bids each day. We analyze the online and offline performances of our approach and discuss how we find it as a production-efficient solution.

翻译：随着搜索引擎营销规模的扩大,设计高效的投标系统对电子商务公司的成功至关重要,现代工业级投标系统面临的关键挑战包括:1. 目录是巨大的,相关的投标特征是高度松散的;2. 大量招标请求给离线和在线服务带来了巨大的计算负担。 利用外部用户项目信息已证明对缓解紧张问题至关重要,我们利用用户查询的自然语言信号和产品背景知识,特别是我们通过变换器模型提取广告的矢量表示,并利用它们与通过集群建立协作投标预测的几何关系。两步程序还大大减少了投标评价和优化的计算压力。在本文件中,我们引入了沃尔玛电子商务搜索引擎营销招标系统的端到端结构,每天成功处理数千万条标书。我们分析了我们做法的在线和离线性表现,并讨论了我们如何把它视为一种生产效率高的解决办法。