Beyond Algorithms: Autonomous Agentic Systems for Personalized Recommendations

Abstract

This paper explores generative AI-based agents for autonomous, personalized content recommendations, utilizing state-of-the-art software for high-performance custom workflows, high-dimensional vector storage and searching, language-based tasks, and autonomous 24/7 running capabilities. Using unstructured, unseen, real-time data from YouTube, we utilize large language models to quantitatively handle subjective tasks and evaluate the outcomes. In essence, we created a recommendation system that uses artificial intelligence to autonomously find content and reduce the time spent on manual search. Content recommendation is a prominent problem in the industry, and we find that the performance of our system is satisfactory, and the scope of such systems is substantial. If used in correlation with default recommendation systems, the system can provide an improved interactive recommendation experience.