User representation is crucial for recommendation systems as it helps to deliver personalized recommendations by capturing user preferences and behaviors in low-dimensional vectors. High-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maintain relevance. The effectiveness of recommendation systems depends significantly on the quality of user embedding. We propose to asynchronously learn high fidelity user embeddings for billions of users each day from sequence based multimodal user activities in Meta platforms through a Transformer-like large scale feature learning module. The async learned user representations embeddings (ALURE) are further converted to user similarity graphs through graph learning and then combined with user realtime activities to retrieval highly related ads candidates for the entire ads delivery system. Our method shows significant gains in both offline and online experiments.

翻译：用户表示对于推荐系统至关重要，它通过将用户偏好和行为捕捉到低维向量中，助力实现个性化推荐。高质量的用户嵌入能够捕捉细微偏好，支持精确的相似度计算，并随时间适应偏好变化以保持相关性。推荐系统的有效性在很大程度上取决于用户嵌入的质量。我们提出了一种异步学习方法，通过类Transformer的大规模特征学习模块，每天从Meta平台上的序列化多模态用户活动中为数十亿用户学习高保真用户嵌入。异步学习的用户表示嵌入（ALURE）进一步通过图学习转化为用户相似度图，并与用户实时活动相结合，为整个广告投放系统检索高度相关的广告候选集。我们的方法在离线和在线实验中均显示出显著提升。