Edit: increased generality.
I have an ad placement optimization problem and I am brainstorming to determine which ML techniques are well suited to it.
Basically, I have some objective that involves optimizing ad placements for a given customer based on historical data on impressions, interactions, revenue and viewership history. There are also certain constraints on the ad placements due to market, content, and repetition etc...
This history goes back over two years and covers a very large customer base. What I would like to do is use machine learning to optimize ad slot placement that maximizes impressions based on the aforementioned inputs.
This sort of problem can be solved using linear programming methods, but I could also see RNNs as a viable option. Any insights here would be greatly appreciated.