  In this work, we present our journey to revolutionize the personalized
recommendation engine through end-to-end learning from raw user actions. We
encode user's long-term interest in Pinner- Former, a user embedding optimized
for long-term future actions via a new dense all-action loss, and capture
user's short-term intention by directly learning from the real-time action
sequences. We conducted both offline and online experiments to validate the
performance of the new model architecture, and also address the challenge of
serving such a complex model using mixed CPU/GPU setup in production. The
proposed system has been deployed in production at Pinterest and has delivered
significant online gains across organic and Ads applications.
