# Recommendations: what and why?

<br />

## What are recommendations?

How does YouTube know what video you might want to watch next? How does the
Google Play Store pick an app just for you? Magic? No, in both cases, an
ML-based recommendation model determines how similar videos and
apps are to other things you like and then serves up a recommendation.
Two kinds of recommendations are commonly used:

- home page recommendations
- related item recommendations

## Homepage recommendations

Homepage recommendations are personalized to a user based on their known
interests. Every user sees different recommendations.

If you go to the Google Play Apps homepage, you may see something like this:
![An image of the Google Play store
homepage that is displaying new and updated games as well as
recommended apps](https://developers.google.com/static/machine-learning/recommendation/images/PlayStore.svg)

## Related item recommendations

As the name suggests, **related items** are recommendations similar to a
particular item. In the Google Play apps example, users looking at a page for
a math app may also see a panel of related apps, such as other math or science
apps.

## Why recommendations?

A recommendation system helps users find compelling content in a large corpus.
For example, the Google Play Store provides millions of apps, while YouTube
provides billions of videos. More apps and videos are added every day. How can
users find new and compelling content? Yes, one can use search to access
content. However, a recommendation engine can display items that users might
not have thought to search for on their own.

> [!IMPORTANT]
> **Did you know?**
>
> - 40% of app installs on Google Play come from recommendations.
> - 60% of watch time on YouTube comes from recommendations.