In this chapter, we started by using regression for rating predictions. We saw a couple of different ways in which to do so, and then combined them all in a single prediction by learning a set of weights. This technique of ensemble learning—and in particular stacked learning—is a general technique that can be used in many situations, not just for regression. It allows 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 mode of producing recommendations: shopping basket analysis, or association rule mining. In this mode, we try to discover (probabilistic) association rules of the form that customers who bought X are likely to be interested in Y. This takes advantage of the data that is generated from sales...
