How to match a user with another user based on their taste? Information available

Consider that there are N users on a platform. Every user adds items that they like on their profile. These items have static attributes that describe the product.

User A:
Row   | Attribute a | Attribute b | Attribute c
Item 1|    0.593    |    0.7852   |   0.484
Item 2|    0.18     |    0.96     |   0.05
Item 3|    0.423    |    0.886    |   0.156

User B:
Row   | Attribute a | Attribute b | Attribute c
Item 7|    0.228    |    0.148    |   0.658
Item 8|    0.785    |    0.33     |   0.887
Item 9|    0.569    |    0.994    |   0.374 


User A has a list of items that he/she likes. Same goes with User B... User N. The items in the profiles of different users might or might not be the same but the items describe the User's taste for that particular item.

Goal

What I want to do is, match a User with another User if they have a similar taste in picking items. I don't understand how to achieve this. Any help is appreciated!
You can perform clustering of your customers based on a distance function.
Definition might look like this:


First, calculate euclidean distances between the first item of the first customer's basket and all of the items in the second customer's basket.
Then find out, what is the closest item from second customer's basket (minimum euclidean distance).
Perform the same operation for each item in first customer's basket.
Calculate mean of the minimum distances.
Do the same for the second customer.
Take maximum of means from the first and the second customer.