In the evolving landscape of digital streaming, content discovery remains one of the most significant challenges for platforms like Netflix.

Traditional recommendation systems rely heavily on algorithmic predictions based on past user behavior, often leading to repetitive suggestions that fail to capture the complexity of human preferences. This project critically examines Netflix’s current recommendation model and proposes an alternative approach that prioritizes dynamic, user-driven categorization over static algorithmic predictions. By integrating social engagement and real-time audience insights, this study explores a more organic and intuitive method of content discovery, aiming to enhance user satisfaction and engagement.