In this chapter, we used Spark's ML and MLlib library to train a collaborative filtering recommendation model, and you learned how to use this model to make predictions for the items that a given user may have a preference for. We also used our model to find items that are similar or related to a given item. Finally, we explored common metrics to evaluate the predictive capability of our recommendation model.
In the next chapter, you will learn how to use Spark to train a model to classify your data and to use standard evaluation mechanisms to gauge the performance of your model.
