With the rapid development of online services and web applications, recommender systems (RS) have become increasingly indispensable for mitigating information overload and matching users’ information needs by providing personalized suggestions over items. Although the RS research community has made remarkable progress over the past decades, conventional recommendation models (CRM) still have some limitations,e.g., lacking open-domain 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 for various natural language processing (NLP) tasks, which mainly stem from their extensive open-world knowledge, logical and commonsense reasoning abilities, 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 common knowledge and capabilities to compensate for the limitations of CRM. In this paper, we conduct a comprehensive survey on this research direction, and draw a bird’s-eye view from the perspective of the whole pipeline in real-world recommender systems. Specifically, we summarize existing research 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 during training, and whether to involve conventional recommendation models for inference. Detailed analysis and general development paths are provided for both “WHERE” and “HOW” questions, respectively. Then, we highlight the 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. To further facilitate the research community of LLM-enhanced recommender systems, we actively maintain a GitHub repository for papers and other related resources in this rising direction1.

Jianghao Lin

Xinyi Dai

Yunjia Xi

Weiwen Liu

Bo Chen

Hao Zhang

Yong Liu

Chuhan Wu

Xiangyang Li

Chenxu Zhu

Huifeng Guo

Yong Yu

Ruiming Tang

Weinan Zhang

ACM Transactions on Information Systems

ACM 信息系统交易 (TOIS) 发表有关信息检索（例如搜索引擎、推荐系统）的论文，其中包含：具有良好经验验证的新的原则性信息检索模型或算法；观察、实验和/或理论研究产生新的见解信息检索或信息搜索；现有信息检索技术的应用说明，阐明了这些技术的优缺点；新的信息检索或信息搜索任务的形式化以及评估这些任务性能的方法；内容的开发（文本、图像、语音、视频等）支持信息检索和信息搜索的分析方法；开发用户信息偏好和交互行为的计算模型；创建和分析信息检索和信息搜索的评估方法； ACM TRANSACTIONS ON INFORMATION SYSTEMS (TOIS) 的信息检索范围因其丰富的创意而吸引了行业从业者，并因其对同事工作的描述而吸引了学术研究人员。

ACM Transactions on Information Systems publishes research papers on information retrieval, such as search engines and recommendation systems. The content includes: well-validated new principled models or algorithms for information retrieval; new insights generated from observational, experimental, and theoretical studies; application examples of existing information retrieval technologies, highlighting their advantages and disadvantages; formalization of new information retrieval or search tasks and methods for evaluating their performance; analytical methods supporting content development (text, images, speech, video, etc.) for information retrieval and search; computational models of user information preferences and interaction behaviors; and the creation and analysis of evaluation methods for information retrieval and search. Due to its rich creativity and in-depth descriptions of peer work, ACM Transactions on Information Systems attracts the attention of both industry practitioners and academic researchers.

《ACM Transactions on Information Systems》发表有关信息检索（如搜索引擎、推荐系统）的研究论文。内容包括：经过良好经验验证的新信息检索模型或算法；通过观察、实验和理论研究获得的新见解；现有信息检索技术的应用示例，阐述其优缺点；新信息检索或信息搜索任务的形式化及其性能评估方法；支持信息检索和搜索的内容开发（文本、图像、语音、视频等）分析方法；用户信息偏好和交互行为的计算模型；信息检索和搜索评估方法的创建与分析。由于其丰富的创意和对同行研究的深入描述，《ACM Transactions on Information Systems》吸引了行业从业者和学术研究人员的关注。

ACM Trans. Inf. Syst.

How Can Recommender Systems Benefit from Large Language Models: A Survey

推荐系统如何从大型语言模型中获益：一项调查

Category	Quartile
计算机科学	2
计算机科学, 计算机信息系统	2

How Can Recommender Systems Benefit from Large Language Models: A Survey

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