Millions of people order items from Amazon, where they save money and time. Recommendation algorithms are learned from customers, ordering preferences and bring them tailored you may also like recommendations, which are suggestions that help the customer update their cart or add interesting items to a wishlist for later.
Building our own recommendations engine is a learning journey, where we hit several objectives along the way. At the problem formulation stage, we learn that recommendations are a collaborative filtering machine learning problem. We will take advantage of the Spark ML collaborative filtering algorithm to implement a recommendations engine that will generate ratings-based recommendations.
Netflix is famous for its movies where you might enjoy the recommendation feature. Back in 2006, Netflix announced a prize of $1 million...
