Content-based recommendation systems

With the advancement of rich, performant technology and more focus on data-driven analytics, recommendation systems are gaining popularity. Recommendation systems are components that provide the most relevant information to end users based on their behavior in the past. The behavior can be defined as a user's browsing history, purchase history, recent searches, and so on. There are many different types of recommendation systems. In this section, we will keep our focus on two categories of recommendation engines: collaborative filtering and content-based recommendation. 

Content-based recommendation systems are the type of recommendation engines that recommend items that are similar to items the user has liked in the past. The similarity of items is measured using features associated with an item. Similarity is basically a mathematical function that can be defined by a variety of algorithms. These types of recommendation systems match user profile attributes...