Effective user representations are pivotal in personalized advertising. However, stringent constraints on training throughput, serving latency, and memory, often limit the complexity and input feature set of online ads ranking models. This challenge is magnified in extensive systems like Meta's, which encompass hundreds of models with diverse specifications, rendering the tailoring of user representation learning for each model impractical. To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models. SUM leverages a few designated upstream user models to synthesize user embeddings from massive amounts of user features with advanced modeling techniques. These embeddings then serve as inputs to downstream online ads ranking models, promoting efficient representation sharing. To adapt to the dynamic nature of user features and ensure embedding freshness, we designed SUM Online Asynchronous Platform (SOAP), a latency free online serving system complemented with model freshness and embedding stabilization, which enables frequent user model updates and online inference of user embeddings upon each user request. We share our hands-on deployment experiences for the SUM framework and validate its superiority through comprehensive experiments. To date, SUM has been launched to hundreds of ads ranking models in Meta, processing hundreds of billions of user requests daily, yielding significant online metric gains and infrastructure cost savings.

翻译：有效的用户表示在个性化广告中至关重要。然而，训练吞吐量、服务延迟和内存的严格限制，往往制约了在线广告排序模型的复杂度和输入特征集。这一挑战在Meta等大型系统中被放大，该系统包含数百个具有不同规格的模型，使得为每个模型定制用户表示学习变得不切实际。为应对这些挑战，我们提出了用户建模扩展（SUM）框架，该框架已广泛部署于Meta的广告排序系统，旨在促进跨数百个广告模型的高效、可扩展的在线用户表示共享。SUM利用少数指定的上游用户模型，通过先进的建模技术，从海量用户特征中合成用户嵌入。这些嵌入随后作为下游在线广告排序模型的输入，促进高效的表示共享。为适应用户特征的动态性并确保嵌入的新鲜度，我们设计了SUM在线异步平台（SOAP），这是一个无延迟的在线服务系统，辅以模型新鲜度和嵌入稳定性，能够支持频繁的用户模型更新以及在每次用户请求时在线推断用户嵌入。我们分享了SUM框架的实战部署经验，并通过全面实验验证了其优越性。截至目前，SUM已在Meta的数百个广告排序模型中上线，每日处理数千亿用户请求，带来了显著的在线指标提升和基础设施成本节约。