With the rapid development of online services, recommender systems (RS) have become increasingly indispensable for mitigating information overload. Despite remarkable progress, conventional recommendation models (CRM) still have some limitations, e.g., lacking open-world knowledge, and difficulties in comprehending users' underlying preferences and motivations. Meanwhile, large language models (LLM) have shown impressive general intelligence and human-like capabilities, which mainly stem from their extensive open-world knowledge, reasoning ability, as well as their comprehension of human culture and society. Consequently, the emergence of LLM is inspiring the design of recommender systems and pointing out a promising research direction, i.e., whether we can incorporate LLM and benefit from their knowledge and capabilities to compensate for the limitations of CRM. In this paper, we conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems. Specifically, we summarize existing works from two orthogonal aspects: where and how to adapt LLM to RS. For the WHERE question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, user interaction, and pipeline controller. For the HOW question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLM or not, and whether to involve conventional recommendation models for inference. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We actively maintain a GitHub repository for papers and other related resources: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys/.

翻译：随着在线服务的快速发展，推荐系统（RS）在缓解信息过载方面日益不可或缺。尽管取得了显著进展，传统推荐模型（CRM）仍存在一些局限性，例如缺乏开放世界知识、难以理解用户的潜在偏好与动机。与此同时，大型语言模型（LLM）展现出令人瞩目的通用智能与类人能力，这主要归功于其广泛的开放世界知识、推理能力，以及对人类文化与社会的理解。因此，LLM的出现正启发着推荐系统的设计，并指明了一个有前景的研究方向：即我们能否融合LLM，利用其知识与能力来弥补CRM的局限。本文从真实推荐系统完整流水线的视角，对这一研究方向进行了全面综述。具体而言，我们从两个正交维度总结了现有工作：在何处（where）以及如何（how）将LLM适配至RS。针对“何处”问题，我们讨论了LLM在推荐流水线不同阶段可能扮演的角色，即特征工程、特征编码器、评分/排序函数、用户交互及流水线控制器。针对“如何”问题，我们探究了训练与推理策略，提出了两个细粒度的分类标准：是否微调LLM，以及是否引入传统推荐模型进行推理。进而，我们从效率、有效性和伦理三个方面，强调了将LLM适配至RS的关键挑战。最后，我们对综述进行了总结，并展望了未来前景。我们持续维护一个存放相关论文及其他资源的GitHub仓库：https://github.com/CHIANGEL/Awesome-LLM-for-RecSys/。