Rethinking Personalized Ranking at Pinterest: An End-to-End Approach
This work addresses the problem of improving personalized recommendations for users on Pinterest, representing an incremental advancement in recommendation systems.
The authors tackled personalized ranking at Pinterest by developing an end-to-end model that encodes long-term interests and short-term intentions from raw user actions, which was deployed in production and delivered significant gains across organic and Ads applications.
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.