10 - Case Study: AI Video Recommender

In this episode, Michael and Ben discuss the intricacies of building a video clipping service and recommendation engine. They explore the challenges of defining interesting clips, the importance of feature requirements, and the cold start problem when launching without user data. The conversation delves into tagging and metadata extraction, hierarchical models for recommendations, and the significance of user telemetry data. A/B testing is highlighted as a crucial method for optimizing user experience and recommendations.