The end goal of a recommendation system is to suggest new items based on a user's historical usage and preferences. The basic idea is to use a ranking for any product that a customer has been interested in in the past. This ranking can be explicit (asking a user to rank a movie from 1 to 5) or implicit (how many times a user visited this page). Whether it is a product to buy, a song to listen to, or an article to read, data scientists usually address this issue from two different angles: collaborative filtering and content-based filtering.
Using this approach, we leverage big data by collecting more information about the behavior of people. Although an individual is by definition unique, their shopping behavior is usually not, and some similarities can always be found with others. The recommended items will be targeted for a particular individual, but they will be derived by combining the user's behavior with that of similar users. This is the famous...