In the previous recipe, we used one out of many possible distance measures to capture the distance between the movie reviews of users. This distance between two specific users is not changed even if there are five or five million other users.
In this recipe, we will compute the correlation between users in the preference space. Like distance metrics, there are many correlation metrics. The most popular of these are Pearson or Spearman correlations or cosine distance. Unlike distance metrics, the correlation will change depending on the number of users and movies.
We will be continuing the efforts of the previous recipes again, so make sure you understand each one.