Designing Twitter-Style Feed Recommendation Systems with Machine Learning

How do massive social platforms decide what content appears on a user's timeline? Building a highly engaging, real-time feed requires a sophisticated blend of system architecture and machine learning. This course guides you through the fundamental building blocks of feed recommendation systems. You will transition from understanding basic system constraints to designing multi-stage recommendation pipelines that process millions of posts in milliseconds. What you'll learn: 1. Understand the core terminology and foundational architecture of modern feed systems. 2. Design candidate generation pipelines using modern vector databases and embedding search. 3. Generate high-quality training data and handle implicit feedback loops without bias. 4. Build and evaluate machine learning ranking models optimized for user engagement. 5. Apply real-time filtering, heavy rankers, and diversity heuristics to final feeds. 6. Practice system design decisions through comprehensive written scenarios and analysis. We begin with the absolute basics, defining key terms and exploring the high-level flow of a feed pipeline. From there, we dive into candidate selection, feature engineering, and the specific machine learning models used to rank content. This course is designed for aspiring system architects, software developers, and data scientists looking to enter the world of recommendation systems. No advanced machine learning background is required to get started. Read on to master the mechanics behind the feeds we use every day.