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.