基于深度学习与大语言模型的序列推荐研究进展
TP31; 推荐系统旨在解决传统信息检索系统中信息过载的问题,并且致力于向用户推荐个性化感兴趣的内容.人与系统交互的行为具有一定的顺序性,在提供推荐时需要将其纳入考虑,这就是序列推荐系统.序列推荐系统通过分析用户行为序列,捕捉用户偏好的动态变化,为电子商务、社交媒体和在线视频等多个领域提供精准的个性化推荐服务.全面阐述了序列推荐系统的当前研究进展,并探讨了其在个性化推荐领域的重要性与应用潜力.定义了序列推荐的研究问题,明确了推荐序列的核心目标和挑战.详细分类并总结了序列推荐的主要技术,包括:基于马尔可夫链的传统方法,该方法在建模用户行为序列时依赖于状态转移概率;深度学习驱动的方法,利用神经网络...
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| Published in | 计算机科学与探索 Vol. 19; no. 2; pp. 344 - 366 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
南京航空航天大学 计算机科学与技术学院/人工智能学院,南京 211106%南京航空航天大学 计算机科学与技术学院/人工智能学院,南京 211106
01.02.2025
南京航空航天大学脑机智能技术教育部重点实验室,南京 211106 智能决策与数字化运营工业和信息化部重点实验室,南京 211106 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1673-9418 |
| DOI | 10.3778/j.issn.1673-9418.2407090 |
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| Abstract | TP31; 推荐系统旨在解决传统信息检索系统中信息过载的问题,并且致力于向用户推荐个性化感兴趣的内容.人与系统交互的行为具有一定的顺序性,在提供推荐时需要将其纳入考虑,这就是序列推荐系统.序列推荐系统通过分析用户行为序列,捕捉用户偏好的动态变化,为电子商务、社交媒体和在线视频等多个领域提供精准的个性化推荐服务.全面阐述了序列推荐系统的当前研究进展,并探讨了其在个性化推荐领域的重要性与应用潜力.定义了序列推荐的研究问题,明确了推荐序列的核心目标和挑战.详细分类并总结了序列推荐的主要技术,包括:基于马尔可夫链的传统方法,该方法在建模用户行为序列时依赖于状态转移概率;深度学习驱动的方法,利用神经网络模型来捕捉长期依赖关系与复杂模式;混合模型方法,结合多种算法来增强推荐系统的准确性和鲁棒性;以及新兴的基于大语言模型的方法,这些方法通过引入预训练的大语言模型来提升对用户行为和推荐内容的理解能力.展望了未来的研究方向,强调了上下文感知、多模态融合、因果推断、垂直领域特定大语言模型以及缓解幻觉问题等研究点的重要性. |
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| AbstractList | TP31; 推荐系统旨在解决传统信息检索系统中信息过载的问题,并且致力于向用户推荐个性化感兴趣的内容.人与系统交互的行为具有一定的顺序性,在提供推荐时需要将其纳入考虑,这就是序列推荐系统.序列推荐系统通过分析用户行为序列,捕捉用户偏好的动态变化,为电子商务、社交媒体和在线视频等多个领域提供精准的个性化推荐服务.全面阐述了序列推荐系统的当前研究进展,并探讨了其在个性化推荐领域的重要性与应用潜力.定义了序列推荐的研究问题,明确了推荐序列的核心目标和挑战.详细分类并总结了序列推荐的主要技术,包括:基于马尔可夫链的传统方法,该方法在建模用户行为序列时依赖于状态转移概率;深度学习驱动的方法,利用神经网络模型来捕捉长期依赖关系与复杂模式;混合模型方法,结合多种算法来增强推荐系统的准确性和鲁棒性;以及新兴的基于大语言模型的方法,这些方法通过引入预训练的大语言模型来提升对用户行为和推荐内容的理解能力.展望了未来的研究方向,强调了上下文感知、多模态融合、因果推断、垂直领域特定大语言模型以及缓解幻觉问题等研究点的重要性. |
| Abstract_FL | The recommendation system aims to solve the problem of overloading in the information retrieval system,and is committed to recommending personalized interest to users.The behavior of human interaction with the system has a cer-tain order.When providing recommendations,the order needs to be taken into consideration.This is the sequence recom-mendation system.The sequence recommendation system analyzes user behavior sequences,captures the dynamic changes of user preferences,and provides accurate personalized recommendation services for many fields such as e-commerce,social media and online videos.This paper provides an overview of the current research progress in sequential recommendation systems and explores their significance and application potential in the field of personalized recommendation.Firstly,the research problem of sequential recommendation is defined,and the core objectives and challenges of recommendation sequences are clarified.Next,the main techniques in sequential recommendation are summarized in detail,including:traditional methods based on Markov chains,which model user behavior sequences by relying on state transition probabilities;deep learning-driven methods,which utilize neural network models to capture long-term dependencies and complex pat-terns;hybrid models,which combine multiple algorithms to enhance the accuracy and robustness of recommendation systems;and emerging methods based on large language models,which improve the understanding of user behavior and recommendation content through the integration of pre-trained large language models.Finally,the future research directions are prospected,with emphasis on the importance of context perception,multimodal fusion,causal inference,specific large language models of vertical fields,alleviating hallucinations,etc. |
| Author | 徐凤如 李博涵 胥帅 |
| AuthorAffiliation | 南京航空航天大学 计算机科学与技术学院/人工智能学院,南京 211106%南京航空航天大学 计算机科学与技术学院/人工智能学院,南京 211106;南京航空航天大学脑机智能技术教育部重点实验室,南京 211106;智能决策与数字化运营工业和信息化部重点实验室,南京 211106 |
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| Author_FL | LI Bohan XU Fengru XU Shuai |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitle_FL | Research Progress on Sequence Recommendation Based on Deep Learning and Large Language Model |
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| Keywords | sequence recommendation 大语言模型 序列推荐 large language model 推荐系统 recommendation system |
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| Publisher | 南京航空航天大学 计算机科学与技术学院/人工智能学院,南京 211106%南京航空航天大学 计算机科学与技术学院/人工智能学院,南京 211106 南京航空航天大学脑机智能技术教育部重点实验室,南京 211106 智能决策与数字化运营工业和信息化部重点实验室,南京 211106 |
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| Title | 基于深度学习与大语言模型的序列推荐研究进展 |
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