Hi,
I’m in the process of studying machine learning at Edx and fast.ai. I’m currently getting a grasp of things, but look for problems that I’m familiar with to better use it in practice.
My wife works in online marketing, and an interesting system used by many companies is how to distribute the traffic and/or change bids for a click based on the data.
So I imagine it like this, a click will have independent variables such as:
ID, timestamp, os, browser, isp, banner ID, IP/CIDR, websiteID, tag, referrer, campaignID … ParameterN
Then based on this, you would route traffic to the best converting “path” for this segment, i.e.
Timeofday -> OS=Android -> campaignId=abcd1234 -> Browser=Android Webview
An example: https://doc.voluum.com/en/auto-optimization.html
On a high level, how would this be attacked?
I learn a lot better when I can connect knowledge to real problems. I guess what I’m struggling with is to take a problem like this, and attack it when only having done online tutorials. Is it something that could be done in deep learning, or more of a statistics problem? Any pointers. I’ve been looking for parallell examples i.e. on traffic lights, sales optimization in stores, but none of it seems applicable to this problem.
Thanks in advance