I have a user-item matrix that I use to train a denoising autoencoder to predict the top-k items to recommend to the different users.
The idea is to corrupt the matrix by erasing a percentage
p of the items that each users bought and train the autoencoder to reconstruct the uncorrupted matrix.
Following the implementation of this paper, I am currently erasing
20% of the bought items.
I was wondering if it is legit to augment the dataset by first erasing the
p=20% to create the "noised" matrix and, successively, use for instance
p=40% and concatenate the two noised matrices and trin the autoencoder to reconstruct a stack of two uncorrupted matrices.
Is it reasonable or is it just an invitation for overfitting?