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. Additionally, these models typically lack an integrated action-prediction task within a point-wise ranking framework, reducing their predictive power. They also rarely address the infrastructure challenges involved in efficiently serving large-scale sequential models. In this paper, 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) integrating a Next Action Loss function for enhanced user action forecasting, and (3) employing scalable, low-latency deployment solutions tailored to handle the computational demands of extended user action sequences.
@article{arxiv.2506.02267,
title = {TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation},
author = {Xue Xia and Saurabh Vishwas Joshi and Kousik Rajesh and Kangnan Li and Yangyi Lu and Nikil Pancha and Dhruvil Deven Badani and Jiajing Xu and Pong Eksombatchai},
journal= {arXiv preprint arXiv:2506.02267},
year = {2025}
}