An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. (arXiv:2201.01815v1 [cs.IR])

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 …

arxiv collaborative collaborative filtering networks study