04117nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001500153050001500168072001600183072002300199072001500222245019200237250001800429264006900447300006500516336002600581337002600607338003600633347002400669490005200693505110300745520092901848650004002777650003202817650002602849650002902875650005202904650002602956650003302982700007803015700007403093700008503167700008103252710003403333773002603367776003603393776003603429776003603465830005203501856004603553978-3-031-55109-3DE-He21320251030113310.0cr nn 008mamaa240430s2024    sz |    s    |||| 0|eng d  a97830315510937 a10.1007/978-3-031-55109-32doi 4aQA76.9.U83 4aQA76.9.H85 7aUYZ2bicssc 7aCOM0790102bisacsh 7aUYZ2thema12aA Human-Centered Perspective of Intelligent Personalized Environments and Systemsh[electronic resource] /cedited by Bruce Ferwerda, Mark Graus, Panagiotis Germanakos, Marko Tkalčič.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVI, 292 p. 33 illus., 27 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aHuman–Computer Interaction Series,x2524-44770 aPart 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.  aThis 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. 0aUser interfaces (Computer systems). 0aHuman-computer interaction. 0aCognitive psychology. 0aArtificial intelligence.14aUser Interfaces and Human Computer Interaction.24aCognitive Psychology.24aIntelligence Infrastructure.1 aFerwerda, Bruce.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aGraus, Mark.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aGermanakos, Panagiotis.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aTkalčič, Marko.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303155108608iPrinted edition:z978303155110908iPrinted edition:z9783031551116 0aHuman–Computer Interaction Series,x2524-447740uhttps://doi.org/10.1007/978-3-031-55109-3