Advancements in Recommender Systems Through the Integration of Generative Adversarial Networks

Abstract

Generative Adversarial Networks (GANs) have become influential in reshaping artificial intelligence, spanning image generation, text synthesis, and music composition. As researchers increasingly integrate GANs into recommendation systems, the imperative to enhance recommendation quality propels this exploration. This article critically examines the current landscape of GAN incorporation in recommendation systems, identifying a fundamental problem: persistent challenges in training stability, mode collapse, scalability, and data privacy concerns. The central issue revolves around effectively utilizing GANs to craft personalized recommendations. Recognizing the significance of overcoming challenges like training instability and mode collapse, this study proposes a solution through the application of conditional GANs. Leveraging user demographics, browsing history, and item attributes, conditional GANs tailor recommendations to individual preferences, addressing the identified problems. To surmount these challenges, ongoing research endeavors diligently aim not only to overcome hurdles but also to enhance the stability and performance of GANs within recommendation systems. This article serves as a comprehensive guide, spotlighting the current state of GANs in recommendation systems, presenting potential solutions, and offering insights into the evolving landscape of research and development in this dynamic field.