Hey, welcome back.
In this video, I'll introduce you to the three types of recommendation systems you are using as a user
every day when you're browsing Netflix or Amazon.
And then we're going to build these three types in the next videos.
The first type of a recommendation system is known as popularity based recommendation system, which
means that if you are a user and you are browsing Netflix but you are not logged in, so Netflix doesn't
know anything about you yet, so it gives you a front page.
So I'm in Netflix.com browse genre, so I'm just browsing movies here so Netflix doesn't know anything
about you, so it will give you a list of the most popular movies.
So it is recommending you based on popularity.
What is being liked from the majority of people?
Same for Amazon.
So it gives you on the front page.
If you are not logged in, it gives you the most popular products, which means that since Amazon doesn't
know anything about you by showing you the most popular products, it's more likely that you will buy
something.
This is the first type of recommendation system.
But then when if you log in to Amazon or to Netflix and your user account has some history, so perhaps
you watched some movies already, so you have some minutes watched in the Netflix database or you purchase
something on Amazon already.
So Amazon and Netflix, they have a database about you and they know what you watched and what you rated
as high and low.
Then Amazon also knows about other users, right?
So it has a database of users.
So by knowing what you like and by knowing what the other user likes, then it can show you potential
products or movies you may want to consume because that other user who liked and watched the same thing,
they enjoyed these potential next movies and products.
So this is known as collaborative filtering.
So this was the second type of a recommendation system.
So so far we have popularity based and collaborative filtering.
The third type of recommendation system is known as content based filtering, which means that if we,
for example, go inside one of these movies, let's let's click on this, this here.
Now, Netflix will recommend similar movies down here below, right?
Because it knows what this movie is about since it has a database of the movie title, the movie description
and other information about that movie, the genre, etcetera.
And then it has the same data for other movies.
So it compares these data and it finds a similarity, it calculates a similarity coefficient, and then
it shows you the movies which are more most similar to that movie.
And this recommendation system is known as content based filtering.
So these are the three types of recommendation systems, popularity based, collaborative and content
based filtering.
Now in the next videos, we're going to implement all these three.
We're going to start with a movie database.
I'm going to introduce you to this database, to this data in the next video.
So that's the first step.
When you deal with data science and machine learning, you want to get to know your data.
And so I'll give you the data and we'll look through them together in the next video.
See you in the next video.