The recommendation system is a classic application of machine learning that aims to predict which item a user will like best. Personalized recommendations play an integral role for e-commerce platforms, with the goal of driving user engagement through item recommendations.

In this workshop, we will build two types of recommendation systems using data from the MovieLens dataset:

1. an item-item recommender using k Nearest Neighbors (kNN) and cosine similarity
2. a top N recommender using matrix factorization

We will also cover the following topics on recommendations:

• collaborative vs. content-based filtering
• implicit vs. explicit feedback
• handling the cold start problem
• evaluation metrics

By the end of this workshop, you will have a better understanding of the different techniques and tools used to build recommendation systems in real-life scenarios.

• Python 3+
• pandas
• numpy
• scipy
• matplotlib
• seaborn
• scikit-learn

You will need to have (1) jupyter installed on your local machine, or (2) a gmail account to access Google Colab, which allows you to run jupyter notebooks in the cloud.

• Documentation
• Notebook
• Slides