  In recommendation systems, 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 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 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 ads delivery system. Our method shows
significant gains in both offline and online experiments.
