Improved recommendations

Remember where we stopped in the previous chapter: with a very basic, but not very good, recommendation system that gave better than random predictions. We are now going to start improving it. First, we will go through a couple of ideas that will capture some part of the problem. Then, what we will do is combine multiple approaches rather than using a single approach in order to be able to achieve a better final performance.

We will be using the same movie recommendation dataset that we started off with in the last chapter; it consists of a matrix with users on one axis and movies on the other. It is a sparse matrix, as each user has only reviewed a small fraction of the movies.

Using the binary matrix of recommendations

One of the interesting conclusions from the Netflix Challenge was one of those obvious-in-hindsight ideas: we can learn a lot about you just from knowing which movies you rated, even without looking at which rating was given! Even with a binary matrix...