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I have sparsely populated matrix of users as rows with columns being categorical answers to various questions ( question are of various domain about preferences / behaviors of the users ) . answers may be be either numerical ( for example an answer to the question "what's the number of children in your household ? " ), or categorical ( "specify your education level ? BS / PHd / etc."). As mentioned the matrix is sparse , and the aim is to infer the missing entries.
do you think matrix factorization techniques ( for example ALS ) could be suitable for solving this ? ( with proper normalization of the response ) and do you suggest another learning algorithm ?
I think ALS is very applicable here. I would imagine it would need to be fine tuned quite a bit though. Unless the questions are correlated, it might yield some weird results. Just because two people with PHD's have one child, does not mean the third would only have one child. Basically, the more the data the better. Recommending movies works so well because all the columns are related to movies, but this should work with enough data.