Database Reference
In-Depth Information
Summary
In this chapter, we used Spark's MLlib library to train a collaborative filtering recommend-
ation model, and you learned how to use this model to make predictions for the items that a
given user might have a preference for. We also used our model to find items that are simil-
ar or related to a given item. Finally, we explored common metrics to evaluate the predict-
ive 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.
