The tutorial will start with an emphasis on learning the concepts behind recommender systems. Then, we will build three variants of recommender systems in Python. More specifically, we will cover:

• Popularity based recommender system
• Classification model based recommender system
• Nearest neighbor based collaborative filtering
• Model based collaborative filtering (Matrix Factorization)

We will also learn how to evaluate and compare different models of recommender systems using precision and recall curves.

A song dataset will be used to recommend songs using the built popularity based and nearest neighbor based recommender systems. Finally, we will use a small toy dataset to illustrate the intuition behind the matrix factorization based recommender system.