  This work explores the reproducibility of CFGAN. CFGAN and its family of
models (TagRec, MTPR, and CRGAN) learn to generate personalized and
fake-but-realistic rankings of preferences for top-N recommendations by using
previous interactions. This work successfully replicates the results published
in the original paper and discusses the impact of certain differences between
the CFGAN framework and the model used in the original evaluation. The absence
of random noise and the use of real user profiles as condition vectors leaves
the generator prone to learn a degenerate solution in which the output vector
is identical to the input vector, therefore, behaving essentially as a simple
autoencoder. The work further expands the experimental analysis comparing CFGAN
against a selection of simple and well-known properly optimized baselines,
observing that CFGAN is not consistently competitive against them despite its
high computational cost. To ensure the reproducibility of these analyses, this
work describes the experimental methodology and publishes all datasets and
source code.
