Abstract: 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 certain order. When providing recommendations, it needs to be considered into consideration. This is the serial recommendation system. The sequence recommendation system analyzes user behavior sequences, capture the dynamic changes of user preferences, and provide accurate personalized recommendation services for many fields such as e-commerce, social media and online videos. This article comprehensively summarizes the current research progress of the sequence recommendation system, and discusses its importance and application potential in the field of personalized recommendation. Firstly, the research issues recommended by sequences are defined; then detailed classification and summarizing the main technologies of sequence recommendation, including traditional methods based on the Markov chain, deep learning driven methods, mixed model methods, and emerging large language based methods. Finally, we will further look forward to the future research direction, emphasizing the importance of context perception, multi mode fusion, causal inference, specific large language models of vertical fields, and alleviating hallucinations. The research in this article not only provides systematic classification and in-depth analysis for the sequence recommendation field, but also provides constructive opinions for future research.

Key words: recommendation system, sequence recommendation, large language model

摘要： 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。本文全面阐述了序列推荐系统的当前研究进展，探讨了其在个性化推荐领域的重要性与应用潜力。首先定义了序列推荐的研究问题；然后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、深度学习驱动的方法、混合模型方法，以及新兴的基于大语言模型的方法。最后进一步展望未来的研究方向，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。本文的研究不仅为序列推荐领域提供了系统的分类和深入的分析，也为未来的研究提供了建设性的意见。

关键词: 推荐系统, 序列推荐, 大语言模型