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User Modeling and User Profiling: A Comprehensive Survey (2402.09660v2)

Published 15 Feb 2024 in cs.AI, cs.HC, cs.IR, cs.LG, and cs.SI

Abstract: The integration of 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.

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Summary

  • The paper presents a novel taxonomy that categorizes user modeling techniques and clarifies core terminology to unify the research field.
  • The paper examines historical paradigm shifts from basic stereotype models to advanced deep learning approaches, emphasizing practical system applications.
  • The paper outlines emerging directions in human-AI collaboration and ethical, privacy-preserving user modeling to inform future research.

Navigating the Evolution and Future Trends of User Modeling and Profiling

User modeling and profiling stand as pivotal components in the development of personalized systems, driving progress in various domains from recommender systems to adaptive e-learning platforms. This comprehensive survey explores the intricate history, current state, emerging trends, and probable future directions of user modeling and profiling research, offering a nuanced exploration grounded in the examination of existing surveys, terminological clarifications, and paradigm shifts.

Historical Perspectives and Paradigm Shifts

Tracing back to the origins of user modeling, we observe a progression from simple stereotype models to sophisticated deep learning approaches. The field's history highlights a persistent focus on understanding user preferences, behaviors, and needs, underscoring the enduring importance of statistical methods and applications in recommender systems and personalized interfaces. Recent paradigm shifts emphasize the integration of implicit data collection methods and advanced machine learning techniques, aligning with evolving technological capabilities and user expectations.

Core Terminology

In navigating the field, it becomes imperative to clarify core terminology. This survey offers novel definitions for "user model" (or "user profile") and "user modeling" (or "user profiling"), aiming to foster coherence and comprehension within the research community. These definitions encapsulate the essence of constructing and utilizing structured representations of individual users' preferences, behaviors, and characteristics to enhance personalized experiences.

Taxonomy of Current Research

Providing a structured taxonomy, this survey outlines the vast landscape of user modeling, categorizing research into user profile content, construction options, data collection approaches, feature extraction, profile representation, modeling techniques, and performance evaluation. It also highlights "beyond-accuracy" approaches that address the ethical, fair, and privacy-preserving dimensions of user modeling, acknowledging the growing importance of aligning research with human-centered and responsible AI principles.

Future Directions

Peering into the future, emergent research directions underscore the significance of human-AI collaboration, advanced user models that consider the user as an active decision-maker, ethical and equitable user modeling, the integration of cognitive sciences, systems understanding user goals, cross-modal user interaction, and addressing broader societal values through ethical considerations in data sovereignty and privacy. These evolving directions emphasize creating more responsive, adaptive, and ethically grounded systems, paving the way for innovations that holistically cater to user needs.

Concluding Reflections

As we traverse the continuing evolution of user modeling and profiling, the discourse underscores a pivotal shift towards developing technologies intricately aligned with human values, needs, and ethical standards. This survey not only traces the historical advancements and current state of the field but also projects future trajectories, foreseeing an era where personalized technology seamlessly integrates into the fabric of human-centric interactions. The journey ahead promises a convergence of technological sophistication and human-oriented principles, heralding a new epoch in personalized system development.

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