Practical Recommender Systems

Chapter 4. Ratings and how to calculate them

Hello, this is your persona speaking, proceed to learn the following:

• Creating user-item matrices.
• Revisiting explicit ratings to discover why they aren’t always good.
• Diving into the mystery of implicit ratings and its creation.
• Learning about an implicit ratings function that translates evidence into ratings.

In this chapter, you’ll transform your users’ behavior to a format that you can use as input for the recommender algorithms. You’ll start by looking at the user-item matrix, which is where most recommender algorithms start. Then you’ll take another look at explicit ratings, the ratings that users add themselves. Implicit ratings are the core of your system, and you’ll look at those next: ...