Abstract
This study investigates the integration of Artificial Intelligence (AI) in Recommendation Systems (RSs), highlighting its impact on the personalization and efficiency of recommendations in diverse digital environments. Through a literature review, the main AI techniques applied to RSs, such as machine learning and neural networks, were analyzed, along with their effects on improving accuracy and real-time adaptation to user preferences. Additionally, ethical challenges associated with AI implementation, such as privacy, algorithmic bias, and transparency, were explored to promote a more responsible and inclusive approach. The results indicate that while AI has the potential to significantly enhance the effectiveness of RSs, it is essential that the development of these technologies is balanced with ethical practices that ensure fairness and diversity in recommendations. This study provides valuable insights for researchers and developers aiming to improve RSs and address the ethical complexities of mass personalization.
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