13 min • 3 lectures
This course provides a technical overview of non-personalized recommendation systems designed for mobile content applications. It begins by addressing the paradox of choice, a phenomenon where an abundance of options leads to user indecision. To solve this, developers implement algorithms that rely on aggregate data rather than individual user history. Key concepts include popularity ranking through trending lists and global top charts. The curriculum also examines content-based filtering, explaining how systems utilize item attributes such as metadata, genre tags, and media duration to calculate similarity and guide discovery for a general audience. Technical implementation focuses on the role of taxonomy and item-to-item similarity. Mapping descriptive tags allows mobile apps to suggest related content and solve the item cold start problem, where new uploads must be discovered without historical engagement data. The final section covers evaluation metrics, including click-through rates, dwell time, and diversity. It explores how developers use these data points to refine algorithms, manage feedback loops, and introduce serendipity into the user experience. By focusing on the calculation of shared attributes, this series demonstrates how to optimize content discovery and maintain a balanced ecosystem without the need for personalized data.
