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.

我们提出一个方法，通过使用多模态用户活动的序列基本特征学习模块进行异步学习，可以为每天数十亿用户学习高逼真的用户嵌入。这些异步学习的用户表示嵌入（ALURE）通过图学习转化为用户相似性图，并与用户实时活动相结合，为整个广告发布系统检索与之高度相关的广告候选项。在线下和在线实验中，我们的方法显示出显著的增益。