I am trying to build a recommender system for coding interview questions. Let's say I have data for interview questions and the features are

tokenized question content, question tags (linked-list, tree, etc.), ratings (likes/total_votes)

I want to build a system where the user can rate the difficulty level of multiple questions from 1 to 10 and recommend questions based on that.

However, I am not sure how to make the recommendation personalized.

How can I make a different recommendation based on how the user rates the difficulty of the question?

I thought about collaborative filtering, but in this case, it's not possible because I won't have other user's data.

Any thoughts?


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