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蓬勃发展，推荐系统已成为日常生活中提供个性化建议、满足用户偏好的重要组成部分。尽管深度神经网络通过建模用户-物品交互并融入文本侧信息显著提升了推荐系统的性能，但基于深度神经网络的方法仍面临诸多局限，例如难以理解用户兴趣和捕获文本侧信息、无法泛化至多种推荐场景、推理预测能力不足等。与此同时，以ChatGPT和GPT4为代表的大语言模型的兴起，凭借其在语言理解与生成等核心任务中的卓越能力，以及令人瞩目的泛化与推理能力，彻底革新了自然语言处理和人工智能领域。因此，近期研究尝试借助大语言模型的能力来增强推荐系统。鉴于推荐系统这一研究方向快速演进，亟需系统性概述现有基于大语言模型的推荐系统，为相关领域研究者提供深入理解。为此，本文从预训练、微调和提示学习三个维度对基于大语言模型的推荐系统进行全面综述。具体而言，我们首先介绍利用大语言模型（作为特征编码器）学习用户和物品表征的代表性方法，继而从预训练、微调和提示学习三种范式出发，综述大语言模型增强推荐系统的最新技术，最后全面探讨这一新兴领域的未来发展方向。