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020 _a9783031551093
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024 7 _a10.1007/978-3-031-55109-3
_2doi
050 4 _aQA76.9.U83
050 4 _aQA76.9.H85
072 7 _aUYZ
_2bicssc
072 7 _aCOM079010
_2bisacsh
072 7 _aUYZ
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245 1 2 _aA Human-Centered Perspective of Intelligent Personalized Environments and Systems
_h[electronic resource] /
_cedited by Bruce Ferwerda, Mark Graus, Panagiotis Germanakos, Marko Tkalčič.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVI, 292 p. 33 illus., 27 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aHuman–Computer Interaction Series,
_x2524-4477
505 0 _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.
520 _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.
650 0 _aUser interfaces (Computer systems).
_964688
650 0 _aHuman-computer interaction.
_964854
650 0 _aCognitive psychology.
650 0 _aArtificial intelligence.
_958441
650 1 4 _aUser Interfaces and Human Computer Interaction.
_966577
650 2 4 _aCognitive Psychology.
650 2 4 _aIntelligence Infrastructure.
700 1 _aFerwerda, Bruce.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
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700 1 _aGraus, Mark.
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700 1 _aGermanakos, Panagiotis.
_eeditor.
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700 1 _aTkalčič, Marko.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aHuman–Computer Interaction Series,
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856 4 0 _uhttps://doi.org/10.1007/978-3-031-55109-3
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