A layperson might not know about the sophisticated machine learning algorithms controlling the high-frequency transactions taking place in the stock exchange. They may also not know about the algorithms detecting online crimes and controlling missions to outer space. Yet, they interact with recommendation engines every day. They are daily witnesses of the recommendation engines picking books for them to read on Amazon, selecting which movies they should watch next on Netflix, and influencing the news articles they read every day. The prevalence of recommendation engines in many businesses requires different flavors of recommendation algorithms.
In this chapter, we will learn about the different approaches used by recommender systems. We will mainly use a sister library to scikit-learn called Surprise. Surprise is a toolkit that implements different collaborative...
