Abstract: Large language models(LLMs) play a key role in recommendation system(RS),such as feature engineering and feature encoding,pre-training and fine-tuning,and prompt learning.Through feature engineering and feature encoding,LLMs improve the personalization and accuracy of the recommendation system,and optimizes the generalization ability and adaptability of the model.Studies show that LLMs can enrich user profiles and extract item features in the feature engineering stage.The pre-training and fine-tuning phases involve training on a large amount of unlabeled data to prepare for downstream task deployment.The prompt learning phase improves the model’s ability to understand and solve recommendation tasks by designing effective instructions and prompts.This paper also discusses the challenges of LLMs in the application of recommendation systems,such as high computational cost,API dependency,and data noise.Researchers are exploring various optimization strategies.The potential development of recommendation system in the future focuses on data enhancement,fine-tuning efficiency improvement,prompt design optimization,and interpretability enhancement.These comprehensive analyses provide a solid theoretical foundation for the continuous development and innovation in the field of recommendation system.

Key words: Recommendation system, Large language models, Feature engineering, Pre-training and fine-tuning, Prompt learning