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...
