  Over the past decades, recommendation has become a critical component of many
online services such as media streaming and e-commerce. Recent advances in
algorithms, evaluation methods and datasets have led to continuous improvements
of the state-of-the-art. However, much work remains to be done to make these
methods scale to the size of the internet.
  Online advertising offers a unique testbed for recommendation at scale. Every
day, billions of users interact with millions of products in real-time. Systems
addressing this scenario must work reliably at scale. We propose an efficient
model (LED, for Lightweight Encoder-Decoder) reaching a new trade-off between
complexity, scale and performance. Specifically, we show that combining
large-scale matrix factorization with lightweight embedding fine-tuning unlocks
state-of-the-art performance at scale. We further provide the detailed
description of a system architecture and demonstrate its operation over two
months at the scale of the internet. Our design allows serving billions of
users across hundreds of millions of items in a few milliseconds using standard
hardware.
