Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant
attention in the domain of Recommendation Systems (RS). These models, trained
on massive amounts of data using self-supervised learning, have demonstrated
remarkable success in learning universal representations and have the potential
to enhance various aspects of recommendation systems by some effective transfer
techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect
of harnessing the power of language models in enhancing recommendation quality
is the utilization of their high-quality representations of textual features
and their extensive coverage of external knowledge to establish correlations
between items and users. To provide a comprehensive understanding of the
existing LLM-based recommendation systems, this survey presents a taxonomy that
categorizes these models into two major paradigms, respectively Discriminative
LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation
(GLLM4Rec), with the latter being systematically sorted out for the first time.
Furthermore, we systematically review and analyze existing LLM-based
recommendation systems within each paradigm, providing insights into their
methodologies, techniques, and performance. Additionally, we identify key
challenges and several valuable findings to provide researchers and
practitioners with inspiration.

本文概论了基于大型语言模型的推荐系统，提出了两种主要类型（分别为判别型 LLM 推荐 DLLM4Rec 和生成型 LLM 推荐 GLLM4Rec），分别从方法、技术和性能等方面对现有的 LLM 推荐系统进行了系统的分类和评估，并指出了主要挑战和有价值的发现。