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.
翻译:在这项工作中,我们展示了通过从原始用户行动中的端到端学习实现个性化推荐引擎革命的旅程。我们将用户对Pinner-Africt的长期兴趣编码成册,Pinner-Africt,这个用户通过新的密集全动作损失为未来长期行动注入最佳功能,通过直接从实时行动序列中学习来捕捉用户的短期意图。我们进行了离线和在线实验,以验证新模型结构的性能,并用生产中的混合CPU/GPU设置来应对为这种复杂模型服务的挑战。提议的系统已在Pinterest 公司生产中投入使用,并在有机和Ads应用中带来了巨大的在线收益。