Scaling User Modeling: Large-scale Online User Representations for Ads
  Personalization in Meta
Wei Zhang Dai Li Chen Liang Fang Zhou Zhongke Zhang Xuewei Wang Ru Li Yi Zhou Yaning Huang Dong Liang Kai Wang Zhangyuan Wang Zhengxing Chen Min Li Fenggang Wu Minghai Chen Huayu Li Yunnan Wu Zhan Shu Mindi Yuan Sri Reddy
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.