This concludes the first part of our journey into recommender systems. They are an extremely important part of today's online business models and their usefulness is ever-growing, in direct relation to the exponential growth of data generated by our connected software and hardware. Recommender systems are a very efficient solution to the information overload problem—or rather, an information filter problem. Recommenders provide a level of filtering that's appropriate for each user, turning information, yet again, into a vector of customer empowerment.
Although it's critical to understand how the various types of recommender systems work, in order to be able to choose the right algorithm for the types of problems you'll solve in your work as a data scientist, implementing production-grade systems by hand is not something most people do. As with almost everything in the realm of software development, it's best to use stable, powerful, and mature existing libraries when they're available...