# Finding the most important questions from a questionary-> results

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.

Edit

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.

• The question seems confusing. Why do you want people to tag articles as good or bad? Are you giving only 20 suggestions combining all 30 questions? or each answer he gives gets 20 suggestions? Maybe give an example to help. Commented Nov 9, 2016 at 11:35
• From the 30 questions 20 suggested articles will be given on total, for each person. And the person will evaluate the articles as relevant or not. Commented Nov 9, 2016 at 12:15
• Of the 30 answers, will you find closest document to each answer (30 documents) and provide 20 closest ones, or sum up all the counts in 30 answers and find the closer 20 documents to the sum. ? Commented Nov 9, 2016 at 13:05
• Good question! Just became one of my additional concern. I was thinking of summing up. Which approach would you follow? Commented Nov 9, 2016 at 13:50

option 1.) find the closest document to each answer(30 docs) and after the documents are validated relative or not. You will have the results of each person's evaluation (Xpersons x 30 scores). Now order the questions in descending order from most scored question to less scored one.