Recommendation Systems Theory and Coding in C++

This beginner-level course path introduces the theory and implementation of recommendation systems in C++. You will begin with baseline prediction models, similarity measures, and foundational algorithms for generating personalized recommendations.

You will explore collaborative filtering, matrix factorization, and Alternating Least Squares (ALS) for both explicit and implicit feedback. You will also build content-based models using item features, user profiles, and factorization machines.

The path concludes with methods for assessing recommendation quality beyond prediction accuracy. Through practical coding examples, you will apply metrics such as coverage, serendipity, novelty, and diversity to evaluate and improve recommendation results.

Syllabus

Implement baseline recommendation models and similarity measures in C++
Build collaborative filtering systems utilizing explicit and implicit feedback
Apply matrix factorization and Alternating Least Squares (ALS) to recommendation data
Create content-based models from item features and user profiles
Use factorization machines to produce personalized recommendations
Evaluate recommendations using coverage, serendipity, novelty, and diversity
Compare recommendation approaches to select suitable methods for different use cases