What you're saying makes sense. I understand that you don't want to base the recommendations based on other users.

I do think you need a specific way to characterize your feature vector though. N-grams is one way but it can blow up the dimensionality really quickly. You'll also have to think about how you want to handle images and other media. Do you also want to include some features representing the profile of the person voting?

I would also be careful of creating your own filter bubble. Maybe create two models: one for maximizing the expected value of a recommendation and another for maximizing the maximum value of a set of recommendations . You could then mix core recommendations with discovery.

I understand that these suggestions only add complexity to any system you build so I only offer them as potential improvements.

In any case, I'd be happy to help you test and give feedback for what you build.