[1]谢广明,白彦冰,吴子昂,等.基于大语言模型的推荐系统综述[J].智能系统学报,2025,20(6):1520-1533.[doi:10.11992/tis.202410007]
XIE Guangming,BAI Yanbing,WU Ziang,et al.Review of LLM-based recommendation systems[J].CAAI Transactions on Intelligent Systems,2025,20(6):1520-1533.[doi:10.11992/tis.202410007]
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XIE Guangming,BAI Yanbing,WU Ziang,et al.Review of LLM-based recommendation systems[J].CAAI Transactions on Intelligent Systems,2025,20(6):1520-1533.[doi:10.11992/tis.202410007]
基于大语言模型的推荐系统综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第6期
页码:
1520-1533
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2025-11-05
- Title:
- Review of LLM-based recommendation systems
- Keywords:
- recommendation system; recommendation models; LLM; prompt engineering; scaling law; visual large model; sequential modeling; deep learning
- 分类号:
- TP391.3
- 摘要:
- 随着社交网络平台和电子商务平台的崛起，工业级个性化推荐系统在移动互联网时代的作用日益显著，对提升用户浏览体验、购物体验以及扩大用户规模起到了不可替代的作用。在推荐系统中，模型发挥着至关重要的作用。随着算力和数据量的增长，模型结构呈现复杂化、大型化趋势，推荐精准度相较于传统推荐模型也有显著提升。以 GPT和DeepSeek 为代表的大语言模型（large language model，LLM）, 不仅显著改善了语言模型的效果，而且助推了提示工程等训练范式的发展。LLM所具备的语义理解和内容生成能力，使其在工业级推荐系统中的落地应用正处于快速发展阶段。本文对LLM和推荐系统的结合点进行调研，梳理了LLM与工业级推荐系统的结合方式，并提出了对LLM和推荐系统结合的展望，以期利用LLM技术提升推荐模型的训练、推理效率和效果。
- Abstract:
- As social networking and e-commerce platforms have grown in popularity, industrial recommendation systems have assumed an increasingly significant role in the mobile internet era. The recommendation system is imperative for enhancing user experience, optimizing shopping experience, and promoting user growth. In the domain of recommendation systems, the role of models is paramount. As computing power and data volume have increased, model structures have become increasingly complex. These models have also improved the accuracy of recommendation systems in comparison to traditional models. Represented by GPT and DeepSeek, LLM has been demonstrated to enhance the efficacy of language models and catalyze the evolution of novel model training paradigms, such as prompt engineering. The rapid advancements in large language model (LLM) capabilities, particularly in semantic understanding and content generation, are poised to transform industrial recommendation systems. This paper reviews the connections between LLM and recommendation systems, then outlines the ways in which LLM can be integrated with industrial recommendation systems. The objective of our work is to leverage technologies associated with LLM to enhance the efficiency and efficacy of recommendation models.
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备注/Memo
收稿日期:2024-10-9。
基金项目:国家自然科学基金项目（62033010, 61603036）.
作者简介:谢广明，教授，博士生导师，中国自动化学会机器人竞赛工作委员会副主任，国际水中机器人联盟创始人，中国仿真学会机器人系统仿真专委会主任委员，主要研究方向为复杂系统动力学与控制、智能仿生机器人多机器人系统与控制。现主持国家自然科学基金重点项目等8项，获发明专利授权20余项。曾荣获国家自然科学奖二等奖、教育部自然科学奖一等奖、吴文俊人工智能科学技术奖创新奖二等奖，发表学术论文200余篇。E-mail：xiegming@pku.edu.cn。;白彦冰，博士研究生，主要研究方向为推荐系统、大模型、智能系统与控制、计算机辅助设计。先后担任新浪、快手、字节跳动等知名公司核心推荐算法团队负责人。E-mail：baiyb@stu.pku.edu.cn。;张艳玲，副教授，中国仿真学会机器人系统仿真专委会委员，主要研究方向为群体智能、演化博弈、博弈学习和推荐系统。主持国家自然科学基金青年基金项目，是国家自然科学基金重点项目的校内负责人。发表学术论文30余篇。E-mail： yanlzhang@ustb.edu.cn。
通讯作者:张艳玲. E-mail：yanlzhang@ustb.edu.cn
更新日期/Last Update:
1900-01-01