Collaborative filtering is a process for providing recommendations to users based on their behavior by analyzing the behaviors of a lot of users. We observe the effects of this algorithm in our day-to-day life in a large number of applications. For example, when you are using streaming services, such as Netflix or YouTube, the platform recommends videos that you may be interested in based on your streaming history. Social networks, such as Twitter and LinkedIn, suggest people for you to follow or connect with based on your current contacts. Services such as Instagram and Facebook curate posts from your friends and tailor your timeline based on the posts that you read or like. As a data scientist, collaborative filtering algorithms are really useful when you are building recommendation systems based on a large amount of user data.
There are various ways...