'How does the recommendation systems deal with very large data?
How are features compared on large systems? For example, when I search on google, does google compare my request against all the web sites? Or just some specific platforms like Netflix or Youtube. Does it scan all the videos one by one to detect how good the videos are for me?
Solution 1:[1]
It doesn't work like that. It is done by Machine Learning. What it does is it takes a lot of information and get similarities data from other people.And then it applies it on your choice.
Solution 2:[2]
That's a great question!
Not all of the services you listed work the same way. Google does something called indexing, which is basically storing websites in a way where they can be looked up much more efficiently. Discord also does this with messages, which you may have noticed if you use it.
Netflix has about 2000 shows, whereas there are millions and millions of websites, and probably billions of Google-indexed pages. So doing a Netflix search is much simpler and probably doesn't require much indexing or fanciness.
If you're interested in the recommendation algorithms sites like Netflix and YouTube use, you might want to look into Collaborative Filtering. It's a pretty simple algorithm, and it's really interesting.
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | Sohan Arafat |
| Solution 2 |