In this final chapter, we will discuss recommender systems in the context of practicality and industrial use. Until now, we have learned about various types of recommender, including knowledge, content, and collaborative filtering-based engines. However, when used in practice, each recommender usually suffers from one shortcoming or another.
We've discussed these shortcomings in the very first chapter (for instance, the novelty problem of content-based engines and the cold start problem of collaborative filters). We also briefly introduced the concept of the hybrid recommender: a robust system that combines various models to combat the disadvantage of one model with the advantage of another. In this chapter, we will build a simple hybrid recommender that combines the content and the collaborative filters that we've built thus far.