# Sparse IR with user feedback

I'm considering a problem framing within an information retrieval context.

I have a sequence of documents that feature different attributes. In the web context, these would be webpages. One attribute could be "is this a top-10 content creator," etc. When we convert multi-labeled values to binary indicators, we end up with a matrix like:

   a b c d e f
A: 1 0 0 0 1 0
B: 0 1 0 0 1 0
C: 0 0 1 0 0 0
D: 0 1 0 0 0 1
E: 0 0 0 0 1 0
F: 0 0 1 0 0 1


We can ask a user a series of progressive questions about their preferences within this dataset. Eg. "Do you care about it being from a top 10 content creator?"

However, these are just preferences. Just because we know the "answer" to a question doesn't immediately invalidate documents with that attribute. It should just bump it down in the eventual ranking.

The task at hand becomes knowing A) which questions to ask to maximize information gain and B) ranking the resulting list. This seems like it would be an existing research area but thus far I haven't been able to find anything on it. Is there a name for this area of algorithm design?