  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. We have also created a GitHub repository to
index relevant papers on LLMs for recommendation,
https://github.com/WLiK/LLM4Rec.
