Sequential models that encode user activity for next action prediction have become a popular design choice for building web-scale personalized recommendation systems. Traditional methods of sequential recommendation either utilize end-to-end learning on realtime user actions, or learn user representations separately in an offline batch-generated manner. This paper (1) presents Pinterest's ranking architecture for Homefeed, our personalized recommendation product and the largest engagement surface; (2) proposes TransAct, a sequential model that extracts users' short-term preferences from their realtime activities; (3) describes our hybrid approach to ranking, which combines end-to-end sequential modeling via TransAct with batch-generated user embeddings. The hybrid approach allows us to combine the advantages of responsiveness from learning directly on realtime user activity with the cost-effectiveness of batch user representations learned over a longer time period. We describe the results of ablation studies, the challenges we faced during productionization, and the outcome of an online A/B experiment, which validates the effectiveness of our hybrid ranking model. We further demonstrate the effectiveness of TransAct on other surfaces such as contextual recommendations and search. Our model has been deployed to production in Homefeed, Related Pins, Notifications, and Search at Pinterest.

翻译：对用户行为进行编码以预测下一步动作的序列模型已成为构建网络级个性化推荐系统的流行设计选择。传统的序列推荐方法要么利用实时用户行为进行端到端学习，要么以离线批处理方式独立学习用户表征。本文：(1) 介绍了Pinterest的Homefeed排序架构——这是我们的个性化推荐产品及最大的用户交互界面；(2) 提出了TransAct——一种从用户实时活动中提取短期偏好的序列模型；(3) 描述了我们结合TransAct端到端序列建模与批处理生成用户嵌入的混合排序方法。这种混合方法使我们既能通过直接学习实时用户活动获得响应性优势，又能利用在更长时间跨度上学习的、成本效益更高的批处理用户表征。我们介绍了消融实验结果、生产化过程中面临的挑战以及在线A/B实验的成效，这些验证了混合排序模型的有效性。我们进一步在上下文推荐和搜索等其他场景中证明了TransAct的有效性。该模型已在Pinterest的Homefeed、相关图钉、通知和搜索功能中部署上线。