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.

用户表示对个性化广告至关重要。为了解决训练吞吐量、服务延迟和内存的严格限制，我们提出了一个名为 Scaling User Modeling (SUM) 的框架，在 Meta 的广告排名系统中广泛部署，旨在实现数百种广告模型之间高效可扩展的在线用户表示共享。SUM 利用几个指定的上游用户模型，通过先进的建模技术从大量的用户特征中合成用户嵌入，然后将这些嵌入作为输入传递给下游的在线广告排名模型，促进了高效的表示共享。我们通过全面的实验验证了 SUM 框架的优越性，并分享了 SUM 框架的实际部署经验。迄今为止，SUM 已在 Meta 的数百种广告排名模型中推出，并每天处理数百亿次用户请求，产生了显著的在线指标增益和基础设施成本节约。

扩展用户建模：大规模在线用户表征用于元广告个性化

Scaling User Modeling: Large-scale Online User Representations for Ads  Personalization in Meta

Modeling powerful interactions is a critical challenge in Click-through rate
(CTR) prediction, which is one of the most typical machine learning tasks in
personalized advertising and recommender systems. Although developing
hand-crafted interactions is effective for a small number of datasets, it
generally requires laborious and tedious architecture engineering for extensive
scenarios. In recent years, several neural architecture search (NAS) methods
have been proposed for designing interactions automatically. However, existing
methods only explore limited types and connections of operators for interaction
generation, leading to low generalization ability. To address these problems,
we propose a more general automated method for building powerful interactions
named AutoPI. The main contributions of this paper are as follows: AutoPI
adopts a more general search space in which the computational graph is
generalized from existing network connections, and the interactive operators in
the edges of the graph are extracted from representative hand-crafted works. It
allows searching for various powerful feature interactions to produce higher
AUC and lower Logloss in a wide variety of applications. Besides, AutoPI
utilizes a gradient-based search strategy for exploration with a significantly
low computational cost. Experimentally, we evaluate AutoPI on a diverse suite
of benchmark datasets, demonstrating the generalizability and efficiency of
AutoPI over hand-crafted architectures and state-of-the-art NAS algorithms.

本文提出一种自动构建强大特征交互的方法，称为 AutoPI，该方法采用了一个更通用的搜索空间，并提取了代表性的手工构建特征交互操作符。该方法在 CTR 预测、个性化广告、推荐系统等各种应用中展示了较高的泛化能力和效率。