Recommendation Systems Theory and Coding in C++

This course introduces foundational algorithms and concepts that form the backbone of recommendation systems. You'll start with simple baseline prediction models and gradually advance to similarity measures and more sophisticated prediction models. Mastering these fundamentals is essential for developing robust and efficient recommendation tools.

Dive into content-based recommendation systems, focusing on feature extraction, similarity measures, and factorization machines. You will learn to utilize item features and user profiles to build personalized models. This course provides hands-on C++ examples, progressing from simple similarity methods to advanced factorization techniques for robust, data-driven recommendations.

This course explores collaborative filtering techniques, which are central to modern recommendation systems. It covers both user-based and item-based collaborative filtering methods, as well as matrix factorization and the powerful Alternating Least Squares algorithm.