Recommendation Systems Theory and Coding in JS

This advanced course path develops the theory and coding skills needed to create recommendation systems in JavaScript. You will begin with baseline prediction models, similarity measures, and the foundational concepts behind personalized recommendations.

You will implement user-based and item-based collaborative filtering, matrix factorization, and Alternating Least Squares. 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. You will use coverage, serendipity, novelty, and diversity metrics to analyze model behavior and guide improvements.

Syllabus

Implement baseline prediction models and similarity measures in JavaScript
Build user-based and item-based collaborative filtering systems
Apply matrix factorization and Alternating Least Squares to recommendation tasks
Develop content-based models using item features and user profiles
Use factorization machines to generate personalized recommendations
Evaluate recommendations with coverage, serendipity, novelty, and diversity metrics
Compare recommendation approaches and identify opportunities for improvement