  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.
