I'm building a neural net to predict the value of a piece of art with a wide range of inputs (size, art medium, etc.) and I would like to include the author as an input as well (it is often a huge factor in the value of a single piece of art).
My current concern is that the name of the author isn't an ideal numerical input for a NN (i.e. If I just code each author with an increasing integer value I will be indirectly assigning more value to authors further down the list -_-). My thoughts were to create separate inputs for all the authors in my dataset and then just use one-hot encoding to better represent the input to the NN.
This approach, however, runs into a problem when an author that is not included in my training data is used as an input to the NN (i.e. a new author). I can get around this with an "other author" input field, but I am worried that this won't be accurate as I would not have trained the the NN for this input (all pieces of art with a valuation have an author).
I haven't fully thought this through but I thought of perhaps training 2 NN's, one for a valuation without an author and one for valuation with an author to ensure I have enough training data for an "authorless valuation" to still be reasonably accurate.
I am still trying to conceptualize the best NN architecture before I get stuck into the implementation so if anyone has any suggestions/comments I would be very grateful!
P.S. I am doing this as a small competition with a friend to test a NN vs the traditional commercial valuation techniques.