Abstract
In the rapidly evolving digital space, staying ahead is pivotal for video platforms. The dynamics of recommendation systems, responsible for curating a tailored experience for millions of users daily, become paramount in this pursuit. This study embarks on a comprehensive journey to dissect, simulate, and optimize the algorithms underpinning these recommendations. The proposed segments delve deeper into the specific objectives of this research endeavor. Analyzing YouTube's existing recommendation algorithms and leveraging a proposed model to create a user-friendly interface for the simulation of these algorithms. Testing and evaluating the efficacy of the enhanced algorithms against a benchmark dataset. The future of digital video platforms is intertwined with the evolution of recommended algorithm By enhancing the way platforms like YouTube recommend videos, this study aspires to contribute significantly to improving user experience and platform efficiency. Keyword: ranking algorithms; video recommendation; Analysis, efficiency
Published Version
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