Learning Apache Mahout

Summary

In this chapter, we learned about the basics of building a recommender system using Mahout. We discussed the idea behind recommender systems, similarity measures, and two paradigms for building the recommender, user-based and item-based. We also discussed a couple of use cases for building a recommender and learned how to measure the efficacy of a recommender system.

In the next chapter, we are going to look at clustering algorithms. We will look at the basic concepts of different clustering algorithms and discuss practical examples.