User-based filtering

The main idea behind user-based filtering is that if we are able to find users that have bought and liked similar items in the past, they are more likely to buy similar items in the future too. Therefore, these models recommend items to a user that similar users have also liked. Amazon's Customers who bought this item also bought is an example of this filter, as shown in the following screenshot:

Imagine that Alice and Bob mostly like and dislike the same video games. Now, imagine that a new video game has been launched on the market. Let's say Alice bought the game and loved it. Since we have discerned that their tastes in video games are extremely similar, it's likely that Bob will like the game too; hence, the system recommends the new video game to Bob.