In this chapter, we started by improving our rating predictions from the previous chapter. We saw a couple of different ways in which to do so and then combined them all in a single prediction by learning how to use a set of weights. These techniques, ensemble or stacked learning, are general techniques that can be used in many situations and not just for regression. They allow you to combine different ideas even if their internal mechanics are completely different; you can combine their final outputs.

In the second half of the chapter, we switched gears and looked at another method of recommendation: shopping basket analysis or association rule mining. In this mode, we try to discover (probabilistic) association rules of the customers who bought X are likely to be interested in Y form. This takes advantage of the data that is generated from sales alone without requiring users to numerically rate items. This is not available in scikit-learn (yet), so we wrote our own code (for a change).

Association rule mining needs to be careful to not simply recommend bestsellers to every user (otherwise, what is the point of personalization?). In order to do this, we learned about measuring the value of rules in relation to the baseline as the lift of a rule. In the next chapter, we will build a music genre classifier.