Different from most conventional recommendation problems, sequential recommendation focuses on learning users' preferences by exploiting the internal order and dependency among the interacted items, which has received significant attention from both researchers and practitioners. In recent years, we have witnessed great progress and achievements in this field, necessitating a new survey. In this survey, we study the SR problem from a new perspective (i.e., the construction of an item's properties), and summarize the most recent techniques used in sequential recommendation such as pure ID-based SR, SR with side information, multi-modal SR, generative SR, LLM-powered SR, ultra-long SR and data-augmented SR. Moreover, we introduce some frontier research topics in sequential recommendation, e.g., open-domain SR, data-centric SR, could-edge collaborative SR, continuous SR, SR for good, and explainable SR. We believe that our survey could be served as a valuable roadmap for readers in this field.

翻译：与大多数传统推荐问题不同，序列推荐通过利用交互项目间的内在顺序和依赖关系来学习用户偏好，已受到研究者和实践者的广泛关注。近年来，该领域取得了巨大进展与成就，亟需新的综述研究。本综述从一个新视角（即项目属性的构建）研究序列推荐问题，并总结了序列推荐中的最新技术，例如纯基于ID的序列推荐、带辅助信息的序列推荐、多模态序列推荐、生成式序列推荐、大语言模型赋能的序列推荐、超长序列推荐以及数据增强的序列推荐。此外，我们介绍了序列推荐中的若干前沿研究主题，例如开放域序列推荐、以数据为中心的序列推荐、云边协同序列推荐、持续序列推荐、向善序列推荐与可解释序列推荐。我们相信本综述能为该领域读者提供有价值的路线图。