Foundations of Recommender Systems: Non-Personalized and Content-Based

Discover how modern platforms suggest the right products, articles, and media to their users. This text-based course introduces the fundamental mechanics behind recommender systems, starting from the absolute basics. You will transition from understanding core recommendation concepts to implementing non-personalized and content-based filtering algorithms. You will gain the skills to analyze datasets, compute product associations, and leverage modern text representations to deliver relevant suggestions. What you'll learn: - Understand the core terminology, business value, and architectural patterns of recommendation engines - Calculate non-personalized recommendations using summary statistics and demographic stereotypes - Implement product association rules to suggest items frequently bought together - Build content-based filtering systems using item metadata and user profiles - Apply modern text vectorization and cosine similarity metrics to match user preferences with content - Evaluate the performance and potential biases of different recommendation strategies The course guides you step-by-step through foundational theory, mathematical formulas, and practical code implementations. You will read clear explanations and work through written exercises designed to solidify your understanding of recommendation logic. This course is designed for aspiring data professionals, software developers, and analytical minds who are new to recommender systems. No prior experience in machine learning is required, though a basic familiarity with Python and data concepts is helpful. Start building smarter, data-driven user experiences today.