  Recommender systems (RSs) now play a very important role in the online lives
of people as they serve as personalized filters for users to find relevant
items from an array of options. Owing to their effectiveness, RSs have been
widely employed in consumer-oriented e-commerce platforms. However, despite
their empirical successes, these systems still suffer from two limitations:
data noise and data sparsity. In recent years, generative adversarial networks
(GANs) have garnered increased interest in many fields, owing to their strong
capacity to learn complex real data distributions; their abilities to enhance
RSs by tackling the challenges these systems exhibit have also been
demonstrated in numerous studies. In general, two lines of research have been
conducted, and their common ideas can be summarized as follows: (1) for the
data noise issue, adversarial perturbations and adversarial sampling-based
training often serve as a solution; (2) for the data sparsity issue, data
augmentation--implemented by capturing the distribution of real data under the
minimax framework--is the primary coping strategy. To gain a comprehensive
understanding of these research efforts, we review the corresponding studies
and models, organizing them from a problem-driven perspective. More
specifically, we propose a taxonomy of these models, along with their detailed
descriptions and advantages. Finally, we elaborate on several open issues and
current trends in GAN-based RSs.
