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This study provides a comprehensive examination of AI-driven recommendation systems, acknowledging their multidisciplinary nature and the various academic perspectives that contribute to understanding their complexity. With a focus on the retail sector, the research investigates user attitudes towards the increasingly personalized consumer environment, particularly in regard to privacy concerns, and examines the consequent impact on customer experiences.
Employing a qualitative research methodology, this study examines existing AI recommendation systems through a comprehensive literature review and in-depth case studies. Prominent online platforms such as Netflix and Amazon, the paper highlights the substantial economic benefits derived from AI personalization. Drawing insights from industry examples, including the infamous Facebook Beacon controversy, the research underscores the fine balance between leveraging personal data for customization and maintaining user privacy. The paper offers a balanced perspective on the advantages of AI in recommendation systems while highlighting the urgent need for ethical frameworks to govern their use.
The findings indicate that advanced personalization techniques contribute to enhanced user satisfaction and improved business outcomes. However, these systems also pose challenges, especially in the realm of user privacy and data ethics.
