用户建模的规模化：Meta 广告个性化中的大规模在线用户表示
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
AI总结：
针对Meta大规模广告系统中用户表示学习难以扩展的问题，提出SUM框架，通过上游模型生成用户嵌入供下游数百个广告模型共享，并配套SOAP平台保证新鲜度，已在生产环境验证显著效果。
摘要：
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，常常制约在线广告排序模型的复杂度和输入特征集。这一挑战在像 Meta 这样的大型系统中被放大，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的规模化（SUM）框架，该框架已广泛部署于 Meta 的广告排序系统中，旨在促进数百个广告模型之间高效且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，支持频繁的用户模型更新以及每次用户请求时的用户嵌入在线推理。我们分享了 SUM 框架的实际部署经验，并通过全面的实验验证了其优越性。迄今为止，SUM 已部署到 Meta 的数百个广告排序模型中，每天处理数千亿的用户请求，带来了显著的在线指标提升和基础设施效率的改善。
英文摘要：
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 improved infrastructure efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
