Integrating Artificial Intelligence with Traditional Recommendation Systems

The merging of artificial intelligence (AI) with conventional recommendation systems is an emerging area that might greatly improve user experience and allow for more tailored content distribution. This chapter provides a thorough analysis of the present state of this integration by drawing on previous studies to clarify the pros, cons, and methods of AI-powered recommendation systems. Important themes arise from the study, which include better suggestion accuracy, better user customization, and less data sparsity. For the purpose of demonstrating the efficacy and practical use of AI-driven recommendation systems in real-world scenarios, two case studies pertaining to movie and crop recommendation systems are also provided. The flexibility of these systems to adapt to changing content contexts and user preferences is demonstrated by the case studies, which also highlight their scalability. To guarantee the ethical and responsible deployment of AI in recommendation systems, we also address concerns related to privacy, fairness, and openness. In conclusion, this study adds to our knowledge of how AI is influencing conventional recommendation systems and where this area of study has room to grow in the future.