Foundation Model for Personalized Recommendation

This blog post presents a foundation model for Netflix's personalized recommendation system, addressing the challenges of scalability and efficiency by centralizing member preference learning and leveraging large-scale interaction data. It draws parallels with large language models, emphasizing a data-centric approach, semi-supervised learning, and sophisticated tokenization of user interactions to enhance prediction accuracy. The model tackles unique hurdles like cold-start for new titles, adopting incremental training methods, and incorporating metadata to improve recommendations without user interaction data. This innovative framework aims to evolve Netflix's recommendation capabilities, ensuring sustainable innovation.