Application of Large Language Models in Personalized Advertising Recommendation Systems
DOI:
https://doi.org/10.5281/zenodo.13144319ARK:
https://n2t.net/ark:/40704/JIEAS.v2n4a19Keywords:
Large Language Models, Personalized Advertising, Recommendation Systems, User Behavior Analysis, Content GenerationAbstract
This paper explores the application of Large Language Models (LLMs) in personalized advertising recommendation systems. It delves into the methodologies of using LLMs to analyze user behavior, generate personalized content, and enhance recommendation accuracy. The study employs a comprehensive data collection and preprocessing framework to ensure the robustness and reliability of the findings. Through a comparative study with traditional recommendation systems, the paper demonstrates the potential advantages of LLMs in improving user engagement and satisfaction. Key performance metrics such as precision, recall, and F1-score are used to evaluate the effectiveness of the LLM-based system. Furthermore, the paper examines the computational challenges and data privacy concerns associated with LLM integration. It also discusses the potential for future advancements in LLM technology to further optimize personalized advertising strategies. The paper concludes with a set of proposed solutions and directions for future research, highlighting the transformative impact of LLMs on personalized advertising.
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