I have previously implemented a Neural Network with Back-Propagation that was able to learn Tic-tac-toe and could go pretty well at Connect-4.
Now I'm trying to do a NN that can make a prediction. The idea is that I have a large set of customer purchase history, so people I can "target" with marketing, others I can't (maybe I just have a credit-card number but no email address to spam). I've a catalogue of products that changes on a monthly basis with daily updates to stock.
My original idea was to use the same NN that I've used before, with inputs like purchased y/n for each product and an output for each product (softmax to get a weighted prediction). But I get stuck at handling a changing catalog. I'm also not sure if I should lump everyone in together or sort of generate a NN for each person individually (but some people would have very little purchase history, so I'd need to use everyone else as the training set).
So I thought I'd need something with some ability to use the purchase data as a sequence, so purchased A, then B, then C etc. But reviewing something like LSTM, I kind of think it's still not right.
Basically, I know how to NN for a game-state sort of problem. But I don't know how to do it for this new problem.
