Building recommendation systems
The input to a recommendation system is the feedback of likes and dislikes, and the output is recommended items based on the feedback. Some of the examples of recommendation systems, are as follows:
Netflix/YouTube: Movie/video recommendations
Amazon.com: Customers Who Bought This Item Also Bought section
Spotify: Music recommendations
Google: News recommendations
Broadly, there are two approaches to build recommendation systems: content-based filtering and collaborative filtering. Let's understand these approaches.
Content-based filtering
Content-based filtering systems build recommenders based on item attributes. Examples of item attributes in movies are the genre, actor, director, producer, and hero. A user's taste identifies the values and weights for an attribute, which are provided as an input to the recommender system. This technique is purely domain-specific and the same algorithm cannot be used to recommend other types of products.
One simple example...
