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Regression –
Recommendations Improved
At the end of the last chapter, we used a very simple method to build a
recommendation engine: we used regression to guess a ratings value. In the
first part of this chapter, we will continue this work and build a more advanced
(and better) rating estimator. We start with a few ideas that are helpful and then
combine all of them. When combining, we use regression again to learn the best
way to combine them.
In the second part of this chapter, we will look at a different way of learning called
basket analysis, where we will learn how to make recommendations. Unlike the
case in which we had numeric ratings, in the basket analysis setting, all we have is
information about shopping baskets, that is, what items were bought together. The
goal is to learn recommendations. You have probably already seen features of the
form "people who bought X also bought Y" in online shopping. We will develop
a similar feature of our own.
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.