Building Recommender Systems with Matrix Factorization

How do streaming platforms and e-commerce sites know exactly what products you want to buy next? Behind these personalized experiences lie recommendation engines powered by matrix factorization and collaborative filtering. This text-based course guides you through the foundational mathematics and practical Python implementations of modern recommendation algorithms. You will transition from understanding basic user-item interactions to building, evaluating, and tuning sophisticated hybrid models that combine multiple data sources for superior accuracy. What you'll learn: - Understand the foundational linear algebra and terminology behind matrix factorization and dimensionality reduction - Build collaborative filtering models using Singular Value Decomposition (SVD) and Alternating Least Squares (ALS) - Implement implicit feedback techniques to handle real-world user behaviors like clicks, views, and dwell time - Design hybrid recommender systems that combine collaborative filtering with content-based filtering to solve the cold-start problem - Apply evaluation metrics such as Precision at K and Mean Average Precision to measure recommendation quality - Explore modern retrieval patterns, including approximate nearest neighbors, to scale your models The course begins with essential mathematical concepts and notation before guiding you through step-by-step code implementations and model evaluation strategies. You will read detailed explanations, analyze Python code snippets, and complete written exercises to solidify your understanding of recommendation engine mechanics. This course is designed for beginner data scientists, software developers, and analytical minds who want to understand recommendation engines from the ground up, with no prior experience in recommender systems required. Start reading today to unlock the power of personalized recommendations in your own projects.