Recommendation systems (RS) have become essential for providing personalized user experiences across various platforms, suggesting items ranging from products to media content. In recent years, the incorporation of Generative Adversarial Networks (GANs), a groundbreaking generative modeling technique introduced in 2014, has significantly enhanced the capabilities of RS. Despite the promise of GANs in recommendation systems, their performance is highly sensitive to the choice of hyperparameters. In this paper, we aim to optimize the hyperparameters of a GAN-based approach for learning user latent factors in matrix factorization, with the goal of improving top-N recommendations. Our study evaluates different Hyperparameters Optimization methods, additionally, we will study the advantages and limitations of each method.