Hands-On Data Science and Python Machine Learning

Understanding movie recommendations with an example

So, what we do with this data? Well, what we want to do is recommend movies for people. The way we do that is we look at all the ratings for a given person, find movies similar to the stuff that they rated, and those are candidates for recommendations to that person.

Let's start by creating a fake person to create recommendations for. I've actually already added a fake user by hand, ID number 0, to the MovieLens dataset that we're processing. You can see that user with the following code:

myRatings = userRatings.loc[0].dropna() 
myRatings 

This gives the following output:

That kind of represents ...