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.

本研究旨在通过端到端的学习，从原始用户行为中进行个性化推荐引擎的革新，通过一种新的密集全行动损失，编码用户的长期兴趣来优化用户嵌入，通过直接学习实时操作序列来捕获用户的短期意图。通过离线和在线实验验证了新模型架构的性能，并解决了在生产中使用混合 CPU/GPU 设置服务这种复杂模型的挑战。