I am trying to determine how to set up a neural net to use in a recommendation engine I am building to recommend a cheese to the user. I have a clear understanding of what the output layer represents (the probability of whether it should recommend cheese 1, 2, 3, etc) but it would be very useful to me if I could also define what the layer before it represents as the weights of each of the features of each cheese (feature 1: creaminess (scale of 0 to 1), feature 2: saltiness (scale of 0 to 1), etc). Being able to extract this layer and have the values signify what I want them to would help me build another part of my project I'm working on. I should mention that the input to the neural net comes from a dataset of what cheeses the user has previously rated and what rating out of 5 they gave it.
I understand that you can define how many layers make up the hidden layers and how many nodes are in each layer, but if I have 20 cheese features and I set the last hidden layer to have 20 nodes, I assume that definitely does not guarantee that each node represents a feature.
Any help would be much appreciated. Thanks