- You’ll learn to combine recommenders to take advantage of the strengths and weaknesses of different types of recommender systems.
- You’ll tour the overall classes of hybrid recommenders.
- You’ll be introduced to ensemble recommenders.
- Having knowledge of ensemble recommenders, you’ll look at how to implement a specific algorithm called feature-weighted linear stacking (FWLS).
Supposedly one of the most energy-efficient cars ever made is a hybrid: a Toyota Prius. At its core, Toyota combines two well-known technologies—the combustion engine and the electric engine.[1] Hybrid recommenders are basically the same idea—you combine recommender algorithms to get a more powerful tool. These not only improve the average result, but also attempt to mitigate the corner cases, where algorithms don’t work well.
1 For more information, see https://en.wikipedia.org/wiki/Toyota_Prius.
Figure 12.1 shows the four most acknowledged classes of recommender systems and their data sources. We’ve talked about each recommender system as something that runs alone and in a silo, but the world is far from being this ordered. To provide recommendations, you need to do a mix, or a hybrid, of more than one system. Also, if you’ve access to more than one of the data sources shown in the figure, it’s a sin not to use all of them!