Scaling User Modeling for Personalized Advertising at Meta

The paper explores the challenges faced by Meta in scaling user modeling for personalized advertising, introducing the Scaling User Modeling (SUM) framework. SUM leverages upstream user models to synthesize user embeddings shared across downstream models, addressing constraints on training throughput, serving latency, and memory in large-scale systems.

Key takeaways for engineers/specialists include the importance of efficient sharing of user representations in personalized advertising systems, the benefits of utilizing upstream models for downstream tasks, and the significance of handling dynamic user features and maintaining embedding freshness for improved performance.

Read full paper: https://arxiv.org/abs/2311.09544

Tags: Personalized Advertising, User Modeling, Deep Learning, Neural Networks