Collaborative filtering: Recommending based on similar users

Welcome

What you should know before watching this course

Using the exercise files

Set up environment

What is a recommendation system?

What can you do with recommendation systems?

Cool uses of recommendation systems

Content-based recommendations: Recommending based on product attributes

Introduction to NumPy, SciPy, and pandas

Think in vectors: How to work with large data sets efficiently

Explore our product recommendation data set

Represent product reviews as a matrix

Recommend by predicting missing user ratings

A simple way to predict missing user ratings

Latent representations of users and products

Code the recommendation system

How matrix factorization works

Use latent representations to find similar products

Explore our system’s recommendations

Use regularization

Measure recommendation accuracy

Make recommendations for existing users

How to handle first-time users

Find similar products

Wrap up