Next-Level Personalization: How 16k+ Lifelong User Actions Supercharge Pinterest’s Recommendations

Pinterest's home feed is crucial for user engagement and discovery, and it uses a two-stage process to rank pins based on user interests and personalized pin relevance. The Pinnability model uses a neural network to consume various pin, context, and user signals, but it has limitations in modeling lifelong user behavior. The TransActV2 model addresses these challenges by leveraging long user sequences, integrating a Next Action Loss function, and employing scalable deployment solutions. TransActV2 can model up to 16,000 user actions, integrates explicit action features, and stores actions losslessly using int8 quantization. The model uses a multi-headed, point-wise multi-task network over a wide and deep stack, and introduces a Next Action Loss function to enhance user action forecasting. The NAL function challenges the model to predict not just engagement probability but also what the user will do next. The model achieves significant improvements in offline and online metrics, including a 13.31% increase in top-3 repin hit and a 6.35% increase in repin. The model's industrial-scale engineering enables efficient serving and deployment, achieving 75-81% lower p99 model run latency and 103-338x end-to-end inference latency reduction. The real-world impact of TransActV2 is massive, with millions more meaningful engagements and significant improvements in user experience.