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

arXiv: https://arxiv.org/abs/2311.09544 · HTML: https://mlsanity.com/paper/2311.09544/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://mlsanity.com/paper/2008.13535/) (2020)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/1706.04148/) (2017)
- [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://mlsanity.com/paper/2205.04507/) (2022)
- [Empirical Analysis of Predictive Algorithms for Collaborative Filtering](https://mlsanity.com/paper/1301.7363/) (2013)
- [TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest](https://mlsanity.com/paper/2306.00248/) (2023)
- [One Person, One Model, One World: Learning Continual User Representation without Forgetting](https://mlsanity.com/paper/2009.13724/) (2020)
- [TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation](https://mlsanity.com/paper/2202.05387/) (2022)
- [ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest](https://mlsanity.com/paper/2205.11728/) (2022)
- [AutoShard: Automated Embedding Table Sharding for Recommender Systems](https://mlsanity.com/paper/2208.06399/) (2022)
- [General-Purpose User Embeddings based on Mobile App Usage](https://mlsanity.com/paper/2005.13303/) (2020)
- [On the Consistency of Average Embeddings for Item Recommendation](https://mlsanity.com/paper/2308.12767/) (2023)
- [A Feature Selection Based on Perturbation Theory](https://mlsanity.com/paper/1902.09938/) (2019)

## Built on by (most cited first, 21 of 21)

- [External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation](https://mlsanity.com/paper/2502.17494/) (2025)
- [You Are What You Bought: Generating Customer Personas for E-commerce Applications](https://mlsanity.com/paper/2504.17304/) (2025)
- [LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation](https://mlsanity.com/paper/2601.20083/) (2026)
- [Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment](https://mlsanity.com/paper/2508.02929/) (2025)
- [FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications](https://mlsanity.com/paper/2511.14865/) (2025)
- [RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models](https://mlsanity.com/paper/2509.20883/) (2025)
- [DashCLIP: Leveraging multimodal models for generating semantic embeddings for DoorDash](https://mlsanity.com/paper/2504.07110/) (2025)
- [ERCache: An Efficient and Reliable Caching Framework for Large-Scale User Representations in Meta's Ads System](https://mlsanity.com/paper/2410.06497/) (2024)
- [Instruction-aware User Embedding via Synergistic Language and Representation Modeling](https://mlsanity.com/paper/2510.11016/) (2025)
- [Query as Anchor: Scenario-Adaptive User Representation via Large Language Model](https://mlsanity.com/paper/2602.14492/) (2026)
- [LoKA: Low-precision Kernel Applications for Recommendation Models At Scale](https://mlsanity.com/paper/2605.10886/) (2026)
- [Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities](https://mlsanity.com/paper/2402.16073/) (2024)
- [Root Causing Prediction Anomalies Using Explainable AI](https://mlsanity.com/paper/2403.02439/) (2024)
- [Large Foundation Model for Ads Recommendation](https://mlsanity.com/paper/2508.14948/) (2025)
- [Decoupled Entity Representation Learning for Pinterest Ads Ranking](https://mlsanity.com/paper/2509.04337/) (2025)
- [LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation](https://mlsanity.com/paper/2605.29280/) (2026)
- [DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction](https://mlsanity.com/paper/2606.10243/) (2026)
- [Don't Contrast the Impossible: Region-Constrained Batching for Contrastive User Modeling on a Local Community Platform](https://mlsanity.com/paper/2607.28971/) (2026)
- [PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks](https://mlsanity.com/paper/2608.21381/) (2026)
- [DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation](https://mlsanity.com/paper/2609.32215/) (2026)
- [CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking](https://mlsanity.com/paper/2610.02460/) (2026)

## Abstract (arXiv)

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.