Session 3: Rethinking Personalized Ranking at Pinterest: An End-to-End Approach 

RecSys 2022 by Jiajing Xu (Pinterest, United States)
In this work, we present our journey to revolutionize the recommendation ranking engine through end-to-end learning from raw user actions. We encode user’s long-term interest in PinnerFormer, a user embedding optimizing for long-term future actions via a new dense all-action loss, and capture user’s short-term intent by directly learning from the realtime 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 applications.