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: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.

翻译：推荐系统（RS）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（NLP）领域，大型语言模型（LLM）展现出惊人的涌现能力（例如指令遵循、推理能力），因此将LLM适配至RS以提升性能和用户体验成为具有前景的研究方向。本文从应用视角对该研究方向进行全面综述。我们首先从两个正交维度总结现有研究工作：在何处（WHERE）以及如何（HOW）将LLM适配至RS。针对"何处"问题，我们探讨LLM在推荐管线不同阶段可扮演的角色，即特征工程、特征编码器、评分/排序函数以及管线控制器。针对"如何"问题，我们研究训练与推理策略，提出两个细粒度分类标准：是否微调LLM，以及是否引入传统推荐模型（CRM）进行推理。针对这两个维度，我们分别给出详细分析与发展路径。随后，我们从效率、效能与伦理三个层面阐述LLM适配至RS的关键挑战。最后总结全文并展望未来方向。我们持续维护该新兴方向的论文与资源GitHub仓库：https://github.com/CHIANGEL/Awesome-LLM-for-RecSys。