Content-based recommendation systems

In content-based recommendations, the recommendation systems check for similarity between the items based on their attributes or content and then propose those items to the end users. For example, if there is a movie and the recommendation system has to show similar movies to the users, then it might check for the attributes of the movie such as the director name, the actors in the movie, the genre of the movie, and so on or if there is a news website and the recommendation system has to show similar news then it might check for the presence of certain words within the news articles to build the similarity criteria. As such the recommendations are based on actual content whether in the form of tags, metadata, or content from the item itself (as in the case of news articles).

Let's try to understand content-based recommendation using the following diagram:

As you can see in the preceding diagram, there are four movies each with a specific director and genre...