Pinterest has developed TransActV2, a new model that enhances personalized recommendations by utilizing over 16,000 user actions, allowing for long-term behavior modeling and improved ranking predictions. Key innovations include a Next Action Loss function for better forecasting and scalable deployment solutions, resulting in significant improvements in user engagement metrics. The model demonstrates substantial gains in both offline and online performance, setting a new benchmark for user sequence modeling in recommendation systems.

Pinterest has developed a user journey framework to enhance its recommendation system by understanding users' long-term goals and interests. This approach utilizes dynamic keyword extraction and clustering to create personalized journeys, which have significantly improved user engagement through journey-aware notifications. The system focuses on flexibility, leveraging existing data and models, while continuously evolving based on user behaviors and feedback.

REGEN introduces a new benchmark dataset aimed at enhancing the capabilities of large language models (LLMs) in generating personalized recommendations through natural language interactions. By augmenting the Amazon Product Reviews dataset with user critiques and contextual narratives, REGEN allows for more nuanced conversational recommendations that adapt to user feedback. The study demonstrates how models like LUMEN can effectively integrate recommendation and narrative generation, paving the way for more intuitive user experiences.