Foundations of Recommendation Systems

Every day, we interact with algorithms that suggest what to watch, read, or buy. Understanding how these recommendation systems work is the first step toward building intelligent, user-centric applications. This text-based course guides you through the core concepts of recommendation engines. You will transition from understanding basic terminology to exploring how modern algorithms process data to make personalized suggestions. What you'll learn: - Understand the fundamental terminology and core architectures of recommendation engines - Implement content-based filtering techniques using item features and metadata - Apply collaborative filtering methods to predict user preferences based on historical behavior - Explore modern vector embeddings and similarity metrics to match users with relevant content - Evaluate recommendation quality using standard performance metrics to ensure accuracy and relevance You will start with foundational definitions and key concepts before moving on to practical algorithms and modern evaluation techniques. Each concept is explained through clear written explanations and structured code snippets. This course is designed for beginners, software developers, and aspiring data analysts who want to learn the basics of recommendation systems without needing prior machine learning experience. Start reading today to learn how to build algorithms that deliver personalized user experiences.