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，序列推荐侧重于通过利用交互项目间的内部顺序和依赖关系来学习用户偏好，这一领域已受到研究者和从业者的广泛关注。近年来，我们见证了该领域的巨大进展与成就，因此有必要进行新的综述。本文从一个新视角（即项目属性的构建）研究序列推荐问题，并总结了序列推荐领域的最新技术，例如：基于纯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，本文还介绍了序列推荐领域的一些前沿研究方向，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、向善序列推荐以及可解释序列推荐。我们相信，本综述能为该领域的研究者提供一份有价值的路线图。