A Human-Centered Perspective of Intelligent Personalized Environments and Systems / edited by Bruce Ferwerda, Mark Graus, Panagiotis Germanakos, Marko Tkalčič.

Format:

Book

Contributor:

Ferwerda, Bruce, editor.

Series:

Human–Computer Interaction Series, 2524-4477

Language:

English

Subjects (All):

User interfaces (Computer systems).

Human-computer interaction.

Cognitive psychology.

Artificial intelligence.

User Interfaces and Human Computer Interaction.

Cognitive Psychology.

Intelligence Infrastructure.

Local Subjects:

User Interfaces and Human Computer Interaction.

Cognitive Psychology.

Intelligence Infrastructure.

Physical Description:

1 online resource (302 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

This book investigates the potential of combining the more quantitative - data-driven techniques with the more qualitative - theory-driven approaches towards the design of user-centred intelligent systems. It seeks to explore the potential of incorporating factors grounded in psychological theory into adaptive/intelligent routines, mechanisms, technologies and innovations. It highlights models, methods and tools that are emerging from their convergence along with challenges and lessons learned. Special emphasis is placed on promoting original insights and paradigms with respect to latest technologies, current research trends, and innovation directions, e.g., incorporating variables derived from psychological theory and individual differences in adaptive intelligent systems so as to increase explainability, fairness, and transparency, and decrease bias during interactions while the control remains with the user.

Contents:

Part I: Theory: Individual differences for intelligent personalized environments

Human factors in user modeling for intelligent systems

The role of human-centred ai in user modeling, adaptation, and personalization – Models, frameworks, and paradigms

Fairness and explainability for enabling trust in AI systems

Part II: Method: User models driven from human factors, inferred from data

Transparent music preference modeling and recommendation with a model of human memory theory

Personalization and individual differences in business data analytics

Inferring Eudaimonia and Hedonia from digital traces

Computational methods to infer human factors for adaptation and personalization using eye tracking

Part III: Practice: The human factors in the center of applications and domains

Coarse-grained detection for personalized online learning interventions

Psychologically-informed design of energy recommender systems: Are nudges still effective in tailored choice environments?- Personalized persuasive technologies in health and wellness: From theory to practice.

Notes:

Includes bibliographical references.

ISBN:

9783031551093

OCLC:

1433026883