Mosaic: A Fleet of User Embedding Specialists for Recommendation at Meta

John Zhiyuan Zheng; Xian Sun; Xiangyang Mou; Yujunrong Ma; Christina You; Michael Jiayuan He; Hrishikesh Paranjape; Aakarsha Agarwal; Hong Li

arXiv · 2607.24015

Mosaic: A Fleet of User Embedding Specialists for Recommendation at Meta

John Zhiyuan Zheng·Xian Sun·Xiangyang Mou·Yujunrong Ma·Christina You·Michael Jiayuan He·Hrishikesh Paranjape·Aakarsha Agarwal·Hong Li

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Abstract

User representation is one of the highest-leverage modeling problems in industrial recommendation systems: a single advancement in how users are encoded can propagate across retrieval, ranking, and integrity tasks at platform scale. Prior industrial user representation work builds either a single user model that emits one or more embedding vectors or a shared backbone with task-specific adaptation. In this paper, we present Mosaic, a foundational user modeling platform that employs a fleet of specialists to learn user embeddings. The fleet comprises four architecturally diverse model families - memorization-driven, dense-heavy, sequential-based, and CoTrain models - each focusing on a distinct facet of user behavior. We developed MRM (Multi-task Relations Mining) and CRL (Cosine Redundancy Loss) techniques to maximize the marginal information contribution of each new specialist. We also introduce CoEval and User Tower Zero-Out, new logging-free embedding evaluation framework that improves development velocity while preserving downstream-aligned accuracy. Our hybrid CPU/GPU, online-and-offline serving stack allows each specialist to choose the adequate serving strategy to meet the freshness, latency, and computational requirements. Mosaic delivers consistent and significant offline NE improvements in addition to online gains.

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John Zhiyuan Zheng, Xian Sun, Xiangyang Mou, Yujunrong Ma, Christina You, Michael Jiayuan He, Hrishikesh Paranjape, Aakarsha Agarwal, Hong Li. 2026-07-27. Mosaic: A Fleet of User Embedding Specialists for Recommendation at Meta. https://arxiv.org/abs/2607.24015

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