  Research and education in machine learning needs diverse, representative, and
open datasets that contain sufficient samples to handle the necessary training,
validation, and testing tasks. Currently, the Recommender Systems area includes
a large number of subfields in which accuracy and beyond accuracy quality
measures are continuously improved. To feed this research variety, it is
necessary and convenient to reinforce the existing datasets with synthetic
ones. This paper proposes a Generative Adversarial Network (GAN)-based method
to generate collaborative filtering datasets in a parameterized way, by
selecting their preferred number of users, items, samples, and stochastic
variability. This parameterization cannot be made using regular GANs. Our GAN
model is fed with dense, short, and continuous embedding representations of
items and users, instead of sparse, large, and discrete vectors, to make an
accurate and quick learning, compared to the traditional approach based on
large and sparse input vectors. The proposed architecture includes a DeepMF
model to extract the dense user and item embeddings, as well as a clustering
process to convert from the dense GAN generated samples to the discrete and
sparse ones, necessary to create each required synthetic dataset. The results
of three different source datasets show adequate distributions and expected
quality values and evolutions on the generated datasets compared to the source
ones. Synthetic datasets and source codes are available to researchers.
