Now that we have sufficient background in machine learning pipelines and the nearest neighbors classifier, let's start the discussion on recommendation engines. In order to build a recommendation engine, we need to define a similarity metric so that we can find users in the database who are similar to a given user. The Euclidean distance score is one such metric that we can use to compute the distance between datapoints. We will shift the discussion toward movie recommendation engines.
Computing the Euclidean distance score
Getting ready
In this recipe, we will see how to compute the Euclidean score between two users.
