论文标题
重新思考Pinterest的个性化排名：一种端到端的方法
Rethinking Personalized Ranking at Pinterest: An End-to-End Approach
论文作者
论文摘要
在这项工作中，我们介绍了通过从原始用户行动中端到端学习来彻底改变个性化推荐引擎的旅程。我们对用户对Pinner的长期兴趣编码了Pinner-hord，该用户通过新的致密全行动损失嵌入了为长期未来动作进行优化的用户，并通过直接从实时动作序列中学习来捕获用户的短期意图。我们进行了离线和在线实验，以验证新模型体系结构的性能，还解决了在生产中使用混合CPU/GPU设置为如此复杂的模型服务的挑战。拟议的系统已在Pinterest中部署在生产中，并在有机和广告应用程序中带来了可观的在线收益。
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.
