Alright, let's talk about how item-based collaborative filtering works. It's very similar to user-based collaborative filtering, but instead of users, we're looking at items.
So, let's go back to the example of movie recommendations. The first thing we would do is find every pair of movies that is watched by the same person. So, we go through and find every movie that was watched by identical people, and then we measure the similarity of all those people who viewed that movie to each other. So, by this means we can compute similarities between two different movies, based on the ratings of the people who watched both of those movies.
So, let's presume I have a movie pair, okay? Maybe Star Wars ...
