> For the complete documentation index, see [llms.txt](https://aaronice.gitbook.io/system-design/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aaronice.gitbook.io/system-design/system-design-problems/recommendation-system.md).

# Design Recommendation System

### Heuristic Solution

In fact, there are lots of hacks we can do to build a simple recommendation system. For instance, based on videos a user has watched, we can simply suggest videos from same authors. We can also suggest videos with similar titles or labels. If we use the popularity (number of comments, shares) as another signal, the recommendation system can work pretty well as a baseline.

### Collaborative filtering

One can hardly avoid [mentioning collaborative filtering (CF)](https://en.wikipedia.org/wiki/Collaborative_filtering), which is the most popular technique used in recommendation systems.

#### user-based collaborative filtering

#### item-based collaborative filtering

### Feature engineering

Q: what features can be used to build the recommendation system?

Example: Youtube Recommendation System

* Like/share/subscribe – As mentioned above, they are strong signs about a user’s preferences.
* Watch time
* Video title/labels/categories
* Freshness

### Infrastructure

**Offline**

> In fact, for most **machine** **learning** **systems**, it’s **common** to use **offline** **pipeline** to **process** **big** **data** as you won’t expect it to finish with few seconds.

**Online**

Fetches more than needed and do **filtering**, **ranking** on the fly

## Reference

Gainlo Blog: [Design a Recommendation System](http://blog.gainlo.co/index.php/2016/05/24/design-a-recommendation-system/)

Wikipedia: [Collaborative Filtering](https://en.wikipedia.org/wiki/Collaborative_filtering)


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