A Review of Generative AI in Recommendation Systems

Traditional machine learning algorithms have paved the way for more sophisticated models mainly in the field of Gen AI. This review compares how Gen AI is used in recommendation systems to traditional AI methods and the application of both in various fields. Generative AI has transformed the systems for recommendation by correcting the flaws of conventional machine learning approaches. This review compares the use of Gen AI and conventional AI in RS throughout various fields, with a focus on GANs and VAEs. These generative models indicate their capabilities and challenges involving low data, cold start, and lack of diversity in recommendations. They also show potential limitations for further research. The report outlines recent works to represent how Gen AI enhances RS performance traces key trends, and addresses relevant emerging concerns with reliability and ethics. The results highlight hybrid approaches, promising further developments of the effective and flexible RSs. To conclude, the paper advocates for further research to be performed.