As the title explains my problem, I'm done with creating a recommendation system that can give me similar users for any given new user. The problem I face is, If I extract the list of products that these similar users liked the most, how do I weight these items and recommend, say only 3 of these items.
You can use some kind of distance metrics.
For example, you identify three users that have the lowest distance from your target user and propose one item from each, or three items from the closest user, whatever works best for your usecase
I guess you've used clustering to cluster the users together. Maybe that using product clustering in conjunction with user clustering can give you good results?