A Review on the Role of Generative AI in Personalization and Recommendation Systems

Utilizing past behaviour data and forecasting future actions, recommender systems offer users individualized service support. Recommender systems have naturally incorporated artificial intelligence (AI), particularly computational intelligence and machine learning techniques and algorithms, to improve prediction accuracy and tackle problems with data sparsity and cold start. In-depth discussions of recommender system fundamentals and current practices are provided in this position paper, along with an analysis of how artificial intelligence (AI) might advance the field’s technological advancements and applications. This study highlights fresh research paths and addresses current research difficulties in addition to reviewing recent theoretical and practical achievements. The work thoroughly examines a range of concerns pertaining to artificial intelligence (AI) recommender systems. Additionally, it assesses the progress made in these systems through the use of AI methods such as neural networks and deep learning, fuzzy approaches, transfer learning, genetic algorithms, evolutionary algorithms, and active learning.