My question is what kind of machine learning models could be used in the case we need to predict something from a large pool of possibilities. For example in a kaggle competition( Instacart challenge ) , they ask to predict what products will a user reorder , based on prior orders.
In this case the spectrum of possible outputs is huge ( there are +40000 possible products )
I have studied some models like CNN and usually the output for this models, is a logits tensor holding the probabilities, the size of the tensor usually corresponds to the possibilities in the classification problem. That are generally small ( maximum something like a hundred in cifar-100
But in this case, seems to me that this is not a good approach, because of the number of possible items.
So this CNN models are appropriated in this kind of tasks? what other approaches could be used?