Recommendation Systems Theory and Coding in C++

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.

This course focuses on metrics specific to recommendation systems, crucial for evaluating and optimizing model performance. You'll delve into recommendation-specific metrics such as Coverage, Serendipity, Novelty, and Diversity. Each metric is presented with theoretical insights and practical coding examples to illustrate their application.