Recommender System Powered by Large Language Models
T. T. Lin, Yung-Ming Li · 2024
The rapid development of AI technologies has made machine interaction a daily norm, such as OpenAI's ChatGPT and Microsoft's Copilot, leading us to develop a personalized book recommender system utilizing Large Language Models (LLMs). By analyzing user behavior, employing natural language processing, and applying a fine-tuned recommender model, this system notably enhances the accuracy of its book suggestions and user satisfaction. Our comprehensive evaluations reveal that this framework significantly surpasses traditional models in delivering personalized content that aligns with users' unique reading preferences and dialogue histories. This research delves into the capabilities of LLMs to offer tailored book recommendations, highlighting the system's ability to synergize user data with book content for improved recommendation precision. It also examines user interactions with LLMs, offering valuable insights for future AI-driven recommender systems.