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（AI）在日常生活中的融入，特别是通过信息检索和推荐系统，推动了先进的用户建模与画像技术的发展，以实现个性化体验。这些技术旨在基于用户与系统交互生成的海量数据，构建准确的用户表征。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。我们回顾了其发展历程，从早期的刻板印象模型追溯到最新的深度学习技术，并提出了一种新颖的分类体系，涵盖该研究领域所有活跃议题（包括近期趋势）。本综述重点强调了向更复杂用户画像方法的范式转变，突出了隐式数据收集、多行为建模以及图数据结构整合等方向。同时，我们讨论了隐私保护技术的迫切需求，以及用户建模中可解释性与公平性的推进诉求。通过审视核心术语的定义，我们试图澄清歧义并提出两个核心术语的新型百科全书式定义，以促进对该领域的清晰理解。此外，我们探索了用户建模在虚假新闻检测、网络安全和个性化教育等领域的应用。本综述为研究人员和从业者提供了全面资源，深入解析用户建模与画像的演进历程，并指导更个性化、合乎伦理且高效的AI系统开发。