Consider, there are 'n' users and they have these attributes and values
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
Items in this dataset can be described using the attributes a, b, and, c. So, the items might or might not be the same for different users but the attributes explain the taste of the user.
Currently, I have data for about 1000 users in this format and I can create a classifier for one user that says whether the user will like the given item or not.
What I want to do is to match users who have similar tastes using the info available above. I don't know much about Recommendation Systems and I'd really appreciate if someone could help me out.