Chapter 6. Recommendation Engine with Apache Mahout
Recommendation engines are probably 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 behavior and make recommendations based on what users with similar behavior liked or purchased.
This chapter will first explain the basic concepts required to understand recommendation engine principles and then demonstrate how to utilize Apache Mahout's implementation of various algorithms to quickly get a scalable recommendation engine. This chapter will cover the following topics:
- How to build a recommendation engine
- Getting Apache Mahout ready
- Content-based approach
- Collaborative filtering approach
By the end of the chapter, you will...
