Abstract: Large language models have emerged as highly effective tools in the field of natural language processing(NLP) and have recently garnered considerable attention in the domain of recommendation systems(RS).These models have undergone extensive training on vast datasets through self-supervised learning,achieving remarkable results in learning universal representations.With efficient transfer techniques such as fine-tuning and prompt tuning,they have the potential to significantly enhance various aspects of recommendation system performance.The crux of leveraging language models to optimize recommendation quality lies in fully utilizing their high-quality text feature representations and extensive external knowledge bases to establish strong connections between items and users.To gain a comprehensive and in-depth understanding of current recommendation systems based on large language models,this paper meticulously categorizes these models into two main types:discriminative large language models for recommendation(DLLM4Rec) and generative large language models for recommendation(GLLM4Rec).Furthermore,the latter is subdivided into constrained generation and free generation,and a detailed summary of relevant research on these two approaches is provide.Additionally,this paper identifies key challenges in this field and shares valuable findings,hoping to provide researchers and practitioners with precious inspiration and insights.

Key words: Large language model, Recommender system, Natural language processing