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

TL;DR

This paper introduces SUM, a scalable framework for sharing user representations across Meta's ads models, improving efficiency and personalization in large-scale online advertising systems.

Contribution

The paper presents SUM, a novel scalable user modeling framework with an online serving system, enabling efficient sharing and updating of user embeddings across numerous ads models at Meta.

Findings

SUM improves online advertising metrics significantly.

The framework processes hundreds of billions of user requests daily.

Deployment demonstrates enhanced efficiency and personalization.

Abstract

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.