Recommendation Systems Theory and Coding in Go

In this course, learners will dive into content-based recommendation systems, focusing on feature engineering, user and item profiling, and factorization machines. These approaches utilize item features and user profiles to make recommendations. The course provides hands-on coding examples to demonstrate how to develop content-based models that harness rich data for personalized 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.