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.

Airbnb employs advanced personalization techniques to enhance user experience, tailoring searches and recommendations based on individual preferences and behaviors. By leveraging data-driven insights, the platform optimizes listings and improves customer engagement, ultimately driving bookings and satisfaction.