Metrics for Feed Ranking and Recommendation Systems

Behind every engaging social feed or content recommendation algorithm lies a complex system of metrics designed to balance user activity with long-term value. Understanding how to define, measure, and optimize these metrics is crucial for building successful recommendation engines. This text-based course guides you through the foundational concepts of feed ranking metrics, teaching you how to align technical algorithms with user satisfaction and business goals. What you'll learn: - Understand the foundational architecture of feed ranking and recommendation systems. - Define key engagement metrics, including click-through rates, dwell time, and interaction rates. - Formulate weighted action models to balance different user behaviors like likes, shares, and comments. - Address common system biases such as position bias, clickbait, and feedback loops. - Compare offline evaluation metrics with online A/B testing methodologies. - Apply multi-objective optimization techniques to balance short-term clicks with long-term user retention. You will start with the basic terminology of recommendation loops and system objectives before progressing to the mathematical formulations of weighted metrics and modern evaluation frameworks. This course is designed for beginner data scientists, software engineers, and product managers looking to understand the mechanics of algorithmic feeds without needing advanced machine learning prerequisites. Start reading today to master the core metrics that drive modern recommendation systems.