Recommendation engines are one of the most applied data science approaches in startups today. There are two principal techniques for building a recommendation system: content-based filtering and collaborative filtering. The content-based algorithm uses the properties of the items to find items with similar properties. Collaborative filtering algorithms take user ratings, or other user behaviors, and make recommendations based on what users with similar behaviors liked or purchased.

In this chapter, we will first explain the basic concepts required to understand recommendation engine principles, and then we will demonstrate how to utilize Apache Mahout's implementation of various algorithms in order to quickly get a scalable recommendation engine.

This chapter will cover the following topics:

• How to build a recommendation engine
• Getting Apache Mahout ready
• The content-based approach
• The collaborative filtering approach

By the end of this chapter, you will have learned about the kind of recommendation engine that is appropriate for our problem and how to quickly implement that engine.