As an Associate Content Strategist at StreamFlix, one of my key tasks involves optimizing content on the platform. A substantial part of my job revolves around using user behavior analytics and leveraging machine learning algorithms to curate personalized recommendation lists.
In one of our significant projects, we developed a new content suggestion algorithm that considered not just users' watch histories but also their search patterns and user feedback. We built this system using TensorFlow and Scikit-learn, implementing a hybrid approach that combined collaborative filtering and content-based filtering.
Post implementation, this personalized strategy resulted in a noteworthy increase in user retention and watch time. Users were offered a more personalized and engaging streaming experience, substantiating the effectiveness of content optimization on streaming platforms.