The need for personalized content has grown considerably with the increasing amount of online information. User profiles, as structured collections of user characteristics and interests, are essential for personalization because they help systems better understand individual preferences and deliver more relevant content. This review examines methods for user profiling and their adaptation over time. We organize existing literature into five categories: User Profile Modeling, Profile Dynamics, Recommendation Systems, Personalized Systems, and Adaptive Systems. Key findings highlight the importance of combining explicit and implicit data collection methods, differentiating between short- and long-term user preferences, and employing techniques such as evolutionary algorithms, context-awareness, and explainability. Additionally, we identify promising areas for future research, including multimodal data integration, scalability, privacy preservation, contextual adaptation, and universal user models. This review aims to help readers navigate the extensive literature and provide insights to support the development of practical applications based on user profiling techniques.
Freitas et al. (Thu,) studied this question.
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