Modeling user action sequences has become a popular focus in industrial recommendation system research, particularly for Click-Through Rate (CTR) prediction tasks. However, industry-scale CTR models often rely on short user sequences, limiting their ability to capture long-term behavior. They also rarely address the infrastructure challenges involved in efficiently serving large-scale sequential models. Additionally, these models typically lack an integrated action-prediction task within a point-wise ranking framework, reducing their predictive power. We introduce TransAct V2, a production model for Pinterest's Homefeed ranking system, featuring three key innovations: (1) leveraging very long user sequences to improve CTR predictions, (2) employing scalable, low-latency deployment solutions tailored to handle the computational demands of extended user action sequences, and (3) integrating a Next Action Loss function for enhanced user action forecasting. To overcome latency and storage constraints, we leverage efficient data-processing strategies and model-serving optimizations, enabling seamless industrial-scale deployment. Our approach's effectiveness is further demonstrated through ablation studies. Furthermore, extensive offline and online A/B experiments confirm major gains in key metrics, including engagement volume and recommendation diversity, showcasing TransAct V2's real-world impact.

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.