Generative AI Recommender System in E-Commerce

In today's information-rich world, recommender systems are essential for helping consumers find relevant products and content. The development of efficient recommender systems is still a challenging endeavor even with their broad use. This research explores different approaches to building recommender systems, emphasizing the use of generative AI to overcome underlying difficulties. Conventional recommender systems, like collaborative filtering, struggle with problems like sparsity limitations and the cold start problem. This paper aims to provide a comprehensive overview of recommender system techniques and algorithms, identify the limitations of existing methods, and highlight open research questions and directions for future development. A thorough and comprehensive literature analysis of recommender system algorithms is part of the process, ensuring the validity and reliability of the research. The Autoencoder technique—which has shown to be highly significant and effective—is used for the evaluation. The review will provide a detailed analysis of potential for improving research on recommender systems, while also thoroughly addressing the primary challenges and drawbacks of current methodologies. Furthermore, by providing insights into the usage of Generative AI—more especially, the Autoencoder technique— to improve recommender system accuracy. The study hopes to make a substantial contribution to the area. Through the identification and resolution of current methods' shortcomings, particularly regarding the incorporation of Generative AI, the study endeavors to widen up the opportunity for recommendations that are more precise, varied, and focused on individuals. It is anticipated that the assessment process's use of Autoencoder will highlight the usefulness and efficiency of the suggested strategy and highlight its significance in the continuous development of recommender systems.