Social Feed Ranking: Fair Evaluation and Experimentation

Social feeds are at the heart of modern digital interaction, but designing them fairly and effectively is a complex challenge. This course provides a foundational understanding of how to systematically evaluate and experiment with social feed ranking algorithms, ensuring balanced, ethical, and high-quality user experiences. By the end, you will be equipped to analyze and improve ranking systems for better platform health and user satisfaction. What you'll learn: Learn the core components and goals of social feed ranking systems. Understand various evaluation metrics, including key guardrail metrics for system stability. Apply robust experimentation techniques like A/B testing and interleaving for ranking changes. Analyze and mitigate fairness and bias issues in ranking algorithms. Implement strategies to promote creator equity and maintain platform health. Practice interpreting experiment results to drive data-informed product decisions. The course begins with foundational concepts of social feed design and moves into practical methods for metric definition and robust experimentation. It then explores critical considerations like fairness, bias, and creator equity within ranking systems. This course is for beginners in machine learning, data science, or product management who want to understand how to build and evaluate fair and effective social feed ranking systems. No prior experience with specific machine learning frameworks or advanced statistics is required. Start building more equitable and engaging social experiences today.