Lets assume that I have 5000 articles and I create the TF-IDF of these articles.
Now I ask som people to answer 30 questions and I create the TF-IDF of these answers from the IDF of the articles and use the cosinus similarity to find the 20 closest articles per person with a weight of 1 for each answer. People can mark these 20 articles as relevant to her/him or not relevant(0 or 1).
So, which approach would you follow to see which questions are the most important in their choices? In my mind it seems like feature selection problem. Or not? Any recommendation?
Generally, what I want to do is reduce the number of questions from 30 -> 5 and I have to find which one are the most important questions.
As @null_9 mentioned in a comment, I am not sure which is the best way of providing the most relevant articles: 1) Summing up the similarity scores from all the answers and give the highest, or 2) find
N closest articles per each question and sample 20 of them and provide to the user.