Let's talk about my personal area of expertise—recommender systems, so systems that can recommend stuff to people based on what everybody else did. We'll look at some examples of this and a couple of ways to do it. Specifically, two techniques called user-based and item-based collaborative filtering. So, let's dive in.

I spent most of my career at amazon.com and imdb.com, and a lot of what I did there was developing recommender systems; things like people who bought this also bought, or recommended for you, and things that did movie recommendations for people. So, this is something I know a lot about personally, and I hope to share some of that knowledge with you. We'll walk through, step by step, covering the following topics:

• What are recommender systems?
• User-based collaborative filtering
• Item-based collaborative filtering
• Finding movie similarities
• Making movie recommendations to people
• Improving the recommender's results