Introduction to Recommender Systems

Ever wonder how services know exactly what movie you want to watch next or which product to suggest? This course demystifies the core technology behind those smart recommendations, giving you the foundational knowledge to build your own. You will gain a solid understanding of the principles driving modern recommender systems. You'll move from theory to implementation, learning how to process user data, apply key algorithms, and measure the effectiveness of your recommendations through practical, text-based exercises. What you'll learn: - Understand the key differences between collaborative filtering, content-based, and hybrid approaches. - Implement user-based and item-based collaborative filtering to predict user preferences. - Apply matrix factorization techniques to uncover latent features in user-item interactions. - Build a content-based recommender by representing items based on their attributes. - Evaluate recommendation quality using standard industry metrics like precision, recall, and RMSE. - Explore how vector embeddings form the basis for more advanced recommendation models. - Recognize common challenges like the cold-start problem and sources of bias in recommender systems. The course begins with essential terminology and concepts before guiding you through the practical steps of building different types of recommenders. Each section builds on the last, ensuring you develop a comprehensive understanding from the ground up. This course is designed for beginners with a basic familiarity with programming. No prior experience in machine learning or recommender systems is required to get started. Begin your journey into building intelligent recommendation engines today.