Is stock replenishment of substitute goods in fact a recommendation system?

Hi there!

I'm currently exploring articles on Neo4j recommendation systems. Here's the problem:

  • in a stock replenishment model for any given item there are at least five to six substitute items;
  • there can also be a case where one "bundled" item can be replaced with several individual parts to get the initial "bundled" item;
  • the problem persist when the inventory/stock needs to be replenished - which substitute item should be ordered for the inventory if there is no initial (original or preferred) item available;
  • if i am not mistaken, this is in fact a problem of a recommendation system, but not for the customers - rather category managers and similar roles;
  • if so, i need to start sketching a new model, but need answers on the questions written below...

Where to start?

  • are there any whitepapers discussing similar problems?
  • my idea is to make a random .csv file with 100 original items, each having 5-10 substitute items;
  • how many attributes for nodes/items should be initially considered? For example - brand, price, category listing (premium, average, budget, etc.)...?
  • what about links?
  • what kind of calculations should be used within GDS to get some kind of a meaningful output? I'm doubting centrality measures could help here.