With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an important component of our daily life, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have made significant advancements in enhancing recommender systems by modeling user-item interactions and incorporating textual side information, DNN-based methods still face limitations, such as difficulties in understanding users' interests and capturing textual side information, inabilities in generalizing to various recommendation scenarios and reasoning on their predictions, etc. Meanwhile, the emergence of Large Language Models (LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), due to their remarkable abilities in fundamental responsibilities of language understanding and generation, as well as impressive generalization and reasoning capabilities. As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems. Given the rapid evolution of this research direction in recommender systems, there is a pressing need for a systematic overview that summarizes existing LLM-empowered recommender systems, to provide researchers in relevant fields with an in-depth understanding. Therefore, in this paper, we conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting. More specifically, we first introduce representative methods to harness the power of LLMs (as a feature encoder) for learning representations of users and items. Then, we review recent techniques of LLMs for enhancing recommender systems from three paradigms, namely pre-training, fine-tuning, and prompting. Finally, we comprehensively discuss future directions in this emerging field.

翻译：随着电子商务和网络应用的繁荣，推荐系统已成为我们日常生活的重要组成部分，提供满足用户偏好的个性化建议。虽然深度神经网络通过建模用户-物品交互和整合文本侧信息，在增强推荐系统方面取得了显著进展，但基于DNN的方法仍面临诸多局限性，例如难以理解用户兴趣和捕捉文本侧信息、无法泛化到各种推荐场景并对其预测进行推理等。与此同时，ChatGPT和GPT4等大语言模型的出现，凭借其在语言理解和生成基本任务中的卓越能力，以及令人印象深刻的泛化和推理能力，彻底改变了自然语言处理和人工智能领域。因此，近期研究尝试利用LLM的力量来增强推荐系统。鉴于推荐系统这一研究方向的快速发展，迫切需要一篇系统性的概述，总结现有的LLM赋能推荐系统，为相关领域研究人员提供深入理解。为此，本文从预训练、微调和提示等不同方面对LLM赋能推荐系统进行了全面综述。具体而言，我们首先介绍了利用LLM（作为特征编码器）学习用户和物品表示的代表性方法。随后，我们从预训练、微调和提示三种范式出发，综述了近期利用LLM增强推荐系统的技术。最后，我们全面讨论了这一新兴领域的未来发展方向。