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This paper introduces a novel method for enhancing product recommender systems, blending unsupervised models like K-means clustering, content-based filtering (CBF), and hierarchical clustering with the state-of-the-art GPT-4 large language model (LLM). The groundbreaking aspect lies in leveraging GPT-4 for model evaluation, utilizing its advanced, natural language understanding to elevate recommendation precision. This approach empowers e-commerce with advanced unsupervised algorithms, while GPT-4 refines semantic understanding and yields more personalized recommendations. Experimental results validate the framework’s superiority, advancing recommender system technology and providing businesses with a scalable solution to optimize product recommendations. View this paper
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