  Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh
interest in generative modelling. They immediately achieved state-of-the-art in
image synthesis, image-to-image translation, text-to-image generation, image
inpainting and have been used in sciences ranging from medicine to high-energy
particle physics. Despite their popularity and ability to learn arbitrary
distributions, GAN have not been widely applied in recommender systems (RS).
Moreover, only few of the techniques that have introduced GAN in RS have
employed them directly as a collaborative filtering (CF) model. In this work we
propose a new GAN-based approach that learns user and item latent factors in a
matrix factorization setting for the generic top-N recommendation problem.
Following the vector-wise GAN training approach for RS introduced by CFGAN, we
identify 2 unique issues when utilizing GAN for CF. We propose solutions for
both of them by using an autoencoder as discriminator and incorporating an
additional loss function for the generator. We evaluate our model, GANMF,
through well-known datasets in the RS community and show improvements over
traditional CF approaches and GAN-based models. Through an ablation study on
the components of GANMF we aim to understand the effects of our architectural
choices. Finally, we provide a qualitative evaluation of the matrix
factorization performance of GANMF.
