This system recommends items to users by taking the similarity of items and user profiles into consideration. In simpler terms, the system recommends items similar to those that the user has liked in the past. The similarity of items is calculated based on the features associated with the other compared items and is matched with the user's historical preferences.
As an example, we can assume that, if a user has positively rated a movie that belongs to the action genre, then the system can learn to recommend other movies from the action genre.
While building a content-based recommendation system, we take into consideration the following questions:
How do we create similarity between items?
How do we create and update user profiles continuously?
This technique doesn't take into consideration the user's neighborhood preferences. Hence, it doesn't require a large user group's preference for items for better recommendation accuracy. It only considers the user's past preferences and the properties/features of the items.