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.

该研究提出了 Pinterest 个性化推荐产品 Homefeed 的排名结构与使用的混合模型 ——TransAct，该模型能够从实时用户活动中提取用户的短期偏好，结合离线生成的用户嵌入向量以提高效率，使用该模型进行的混合排序能够有效优化推荐效果并在相关产品实现部署。