I want to build a classifier in Keras that predicts the next item bought by a customer (i.e. multiclass classification). One of the features I intend to input to the model will be the last item bought by a particular customer. My problem is that the list of possible items is extremely large, several tens of thousands. With that in mind, I'm going to feed the ItemID feature into an Embedding Layer in Keras, and concatenate that with other features before running it through the model.
My question is, can I use the Keras shared layer functionality to also embed the labels for the training data (since they are all from the same vocabulary as that embedded ItemID input), such that instead of having a softmax output of n_classes I have an output of n_dimensions, where n_dimensions is however many dims I decided to set that Embedding layer too?