Recommendations have become one of the staples of online services and commerce. This type of automated system can provide each user with a personalized list of suggestions (be it a list of products to purchase, features to use, or new connections). In this chapter, we will see the basic ways in which automated recommendation generation systems work. The field of generating recommendations based on consumer input is often called collaborative filtering, as the users collaborate through the system to find the best items for each other.

In the first part of this chapter, we will see how we can use past product ratings from consumers to predict new ratings. We start with a few ideas that are helpful and then combine all of them. When combining them, we use regression to learn the best way in which they can be combined. This will also allow us to explore a generic concept in machine learning: ensemble learning.

In the second part of this chapter, we will take a look at a different way of learning recommendations: basket analysis. Unlike the cases in which we have numeric ratings, in the basket analysis setting, all we have is information about the shopping baskets—that is, what items were bought together. The goal is to learn about recommendations. You have probably already seen recommendations along the lines of, people who bought X also bought Y, in online shopping. We will develop a similar feature of our own. In summary, this chapter will cover the following:

• Different ways of building recommendation systems by predicting product ratings.
• Stacking as a way of combining multiple predictions. This is a general technique for combining machine learning methods.
• Basket analysis and association-rule mining to build predictions based solely on which items were consumed together.