Recommender systems represent a very active and dynamic field of study. They started initially as a marginal application of machine learning algorithms and techniques, but due to their practical business value, they have become mainstream in recent years. These days, almost all major programming languages provide powerful recommendations systems libraries—and all major online businesses employ recommenders in one form or another.
Julia is a great language for building recommenders due to its excellent performance. Despite the fact that the language is still young, we already have a couple of interesting packages to choose from.
Now, you have a solid understanding of the model-based recommendation systems and of their implementation workflow—both on a theoretical and practical level. Plus, throughout our journey, we've also been exposed to more advanced data wrangling using DataFrames, an invaluable tool in Julia's data science arsenal.
In the next chapter, we'll further improve our...