In this chapter, we learned about recommendation engines. We saw the two types of recommendation engines, that is, content recommenders and collaborative filtering recommenders. We learned how content recommenders can be built on zero to no historical data and are based on the attributes present on the item itself, using which, we figure out the similarity with other items and recommend them. Later, we worked on a collaborative filtering example using the same MovieLens dataset and the Apache Spark alternating least square recommender. We learned that collaborative filtering is based on historical data of users' activity, based on which other similar users are figured out and the products they liked are recommended to the other users.
In the next chapter, we will learn two important algorithms that are part of the unsupervised learning world and they will help us form clusters or groups in unlabeled data. We will also see how these algorithms help us segment the important customers...
