Recommender systems have become indispensable tools for users seeking relevant and personalized content in today's information-saturated landscape. Generative AI, a rapidly advancing subfield of artificial intelligence, holds the potential to revolutionize recommender systems by overcoming their limitations and enhancing their capabilities. This article delves into the various ways generative AI can contribute to more efficient, versatile, and accurate recommender systems.
1. Background: Generative AI and Recommender Systems
Generative AI models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), excel at generating novel, high-quality data by learning from existing samples. Their ability to create new data can significantly benefit recommender systems, which rely on data to understand user preferences and make accurate suggestions.