Paper
Rethinking Personalized Ranking at Pinterest: An End-to-End Approach
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 onli · Authors: Jiajing Xu, Andrew Zhai, Charles Rosenberg · Published 2022-09-18 · DOI 10.1145/3523227.3547394
- Published
- 2022-09-18
- Authors
- 3
- arXiv
- 2209.08435
- Category
- cs.IR
Abstract
arXiv:2209.08435
Jiajing Xu, Andrew Zhai, Charles Rosenberg
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.
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