In this chapter, you learned the basic concepts of recommendation engines, the difference between collaborative and content-based filtering, and how to use Apache Mahout, which is a great basis to create recommenders as it is very configurable and provides many extension points. We looked at how to pick the right configuration parameter values, set up rescoring, and evaluate the recommendation results.

With this chapter, we completed data science techniques to analyze customer behavior that started with customer-relationship prediction in Chapter 4, Customer Relationship Prediction with Ensembles, and continued with affinity analytics in Chapter 5, Affinity Analysis. In the next chapter, we will move on to other topics, such as fraud and anomaly detection.