The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.

翻译：人工智能融入日常生活，特别是通过信息检索与推荐系统，推动了先进的用户建模与画像技术发展，以提供个性化体验。这些技术旨在基于用户与系统交互过程中产生的大量数据，构建精确的用户表征。本文对用户建模与画像研究的现状、演变及未来方向进行了全面综述。我们梳理了历史发展脉络，追溯了从早期刻板模型到最新深度学习技术的演进过程，并提出了一种涵盖该领域所有活跃主题（包括近期趋势）的新型分类体系。本综述聚焦于向更复杂用户画像方法的范式转变，强调了隐式数据收集、多行为建模以及图数据结构的整合。同时，我们探讨了隐私保护技术的迫切需求，以及用户建模方法中可解释性与公平性的推进方向。通过审视核心术语的定义，我们试图澄清歧义，并针对主要术语提出两项全新的百科全书式定义，以促进对该领域的更清晰理解。此外，我们还探讨了用户建模在假新闻检测、网络安全及个性化教育等多个领域的应用。本综述可作为研究人员与实践者的综合参考资源，为理解用户建模与画像的演进提供洞见，并指导更个性化、更符合伦理及更高效的人工智能系统的发展。