Introduction
A recommendation engine is a model that can predict what a user may be interested in. When we apply this to the context of movies, this becomes a movie-recommendation engine. We filter items in our database by predicting how the current user might rate them. This helps us in connecting the users with the right content in our dataset. Why is this relevant? If you have a massive catalog, then the users may or may not find all the relevant content. By recommending the right content, you increase consumption. Companies such as Netflix heavily rely on recommendations to keep the user engaged.
Recommendation engines usually produce a set of recommendations using either collaborative filtering or content-based filtering. The difference between the two approaches is in the way the recommendations are mined. Collaborative filtering builds a model from the past behavior of the current user as well as ratings given by other users. We then use this model to predict what this user might be...
