With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences. Although deep neural networks (DNNs) have made significant progress in improving recommendation systems by simulating the interaction between users and items and incorporating their textual information, these DNN-based approaches still have some limitations, such as the difficulty of effectively understanding users' interests and capturing textual information. It is not possible to generalize to different seen/unseen recommendation scenarios and reason about their predictions. At the same time, the emergence of large language models (LLMs), represented by ChatGPT and GPT-4, has revolutionized the fields of natural language processing (NLP) and artificial intelligence (AI) due to their superior capabilities in the basic tasks of language understanding and generation, and their impressive generalization and reasoning capabilities. As a result, recent research has sought to harness the power of LLM to improve recommendation systems. Given the rapid development of this research direction in the field of recommendation systems, there is an urgent need for a systematic review of existing LLM-driven recommendation systems for researchers and practitioners in related fields to gain insight into. More specifically, we first introduced a representative approach to learning user and item representations using LLM as a feature encoder. We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems from the three paradigms of pre-training, fine-tuning, and prompting. Finally, we had a comprehensive discussion on the future direction of this emerging field.

翻译：随着电子商务和网络应用的蓬勃发展，推荐系统已成为我们日常生活的重要组成部分，能够基于用户偏好提供个性化推荐。尽管深度神经网络通过模拟用户与物品的交互并融合文本信息，在改进推荐系统方面取得了显著进展，但这些基于DNN的方法仍存在某些局限性，例如难以有效理解用户兴趣和捕捉文本信息，无法泛化到不同的已知/未知推荐场景并对其预测进行推理。与此同时，以ChatGPT和GPT-4为代表的大型语言模型的出现，凭借其在语言理解和生成等基础任务上的卓越能力，以及令人印象深刻的泛化与推理能力，彻底革新了自然语言处理和人工智能领域。因此，近期研究致力于利用LLM的力量来改进推荐系统。鉴于这一研究方向在推荐系统领域的快速发展，相关领域的研究人员和从业者迫切需要系统性地回顾现有基于LLM的推荐系统以获取深入见解。更具体地，我们首先介绍了利用LLM作为特征编码器来学习用户和物品表征的代表性方法。随后，我们从预训练、微调和提示三种范式出发，综述了最新LLM技术用于增强协同过滤推荐系统的进展。最后，我们对这一新兴领域的未来方向进行了全面讨论。