Hybrid recommenders

As the name suggests, hybrid recommenders are robust systems that combine various types of recommender models, including the ones we've already explained. As we've seen in previous sections, each model has its own set of advantages and disadvantages. Hybrid systems try to nullify the disadvantage of one model against an advantage of another.

Let's consider the Netflix example again. When you sign in for the first time, Netflix overcomes the cold start problem of collaborative filters by using a content-based recommender, and, as you gradually start watching and rating movies, it brings its collaborative filtering mechanism into play. This is far more successful, so most practical recommender systems are hybrid in nature.

In this book, we will build a recommender system of each type and will examine all of the advantages and shortcomings described in the previous sections.