|Problems? Is your data what you think it is?|
OT: predicting related contentby gwhite (Friar)
|on Jan 03, 2014 at 15:39 UTC||Need Help??|
gwhite has asked for the
wisdom of the Perl Monks concerning the following question:
I have a list of 4k-5k items, I have a short survey (5-6) questions and a lot of users of the survey.
I am trying to get my brain wrapped around the best way to show the items most likely to get a response from the user based on the results of the survey. For instance a 21 year old Male that is active is likely to be interested in workout equipment, but a 51 year old Male that is active is likely to be interested in Ibuprofen.
Right now, I can only come up with brute force testing, and after enough iterations declaring a winner, for each response set. But would it be better to do a pass based on Male first and get a subset then to test against Male+Age, or should I test all the individual response possibilities, then start testing those first round winners with multiple responses?
How do updates to the item list get tested out?
The buzz words multi-variate and predictive modeling get thrown around in discussions on this, searching CPAN did not generate any module results based on those terms that do what I want(or they were so far over my head I didn't get that they are doing what I want), is there a better term for what I am trying to do?