Recommender systems (RS) play important roles to match users' information needs for Internet applications. In natural language processing (NLP) domains, large language model (LLM) has shown astonishing emergent abilities (e.g., instruction following, reasoning), thus giving rise to the promising research direction of adapting LLM to RS for performance enhancements and user experience improvements. In this paper, we conduct a comprehensive survey on this research direction from an application-oriented view. We first summarize existing research works from two orthogonal perspectives: 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, 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 LLMs or not, and whether to involve conventional recommendation model (CRM) for inference. Detailed analysis and general development trajectories are provided for both questions, respectively. 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 also actively maintain a GitHub repository for papers and other related resources in this rising direction: $\href{https://github.com/CHIANGEL/Awesome-LLM-for-RecSys}{[GitHub\;Link]}$.

翻译：推荐系统在互联网应用中扮演着匹配用户信息需求的重要角色。在自然语言处理领域，大型语言模型展现出惊人的涌现能力（如指令遵循、推理），这使得将LLM适配到推荐系统中以提升性能与用户体验成为极具前景的研究方向。本文从应用导向视角对该研究方向进行了全面综述。首先，我们从两个正交维度总结现有研究工作：在何处以及如何将LLM适配至推荐系统。针对“何处”问题，我们探讨了LLM在推荐流程不同阶段可能扮演的角色，即特征工程、特征编码器、评分/排序函数及流程控制器。针对“如何”问题，我们研究了训练与推理策略，提出两种细粒度分类标准：是否微调LLM，以及是否引入传统推荐模型进行推理。针对两个问题，我们分别提供了详细分析及通用发展轨迹。随后，我们从效率、效能与伦理三方面重点阐述了将LLM适配至推荐系统的关键挑战。最后，我们对综述进行总结并展望未来前景。我们还持续维护一个GitHub仓库，收录该前沿方向的相关论文与资源：$\href{https://github.com/CHIANGEL/Awesome-LLM-for-RecSys}{[GitHub\;链接]}$。