We introduced Artificial Intelligence (AI) along with machine learning (ML) in the previous chapter. A recommendation engine could be treated as an AI-driven solution or a simple collection of conditional statements. Building a system that takes in user data and returns the options that best satisfy that input is a complex task. Incorporating ML into such a task might sound quite reasonable.
However, you should take into account the fact that a recommendation engine might comprise a list of rules by which data is processed before being output to the end user. Recommendation engines can run in both expected and unexpected places. For example, when browsing products on Amazon, a recommendation engine suggests products to us based on the product that we are currently viewing. Movie databases suggest new movies based on the movies we have previously...
