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1. Theory-grounded user modeling for personalized HCI

  • Mark P. Graus und Bruce Ferwerda
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Personalized Human-Computer Interaction
Ein Kapitel aus dem Buch Personalized Human-Computer Interaction

Abstract

Personalized systems are systems that adapt themselves to meet the inferred needs of individual users. The majority of personalized systems mainly rely on data describing how users interacted with these systems. A common approach is to use historical data to predict users’ future needs, preferences, and behavior to subsequently adapt the system to these predictions. However, this adaptation is often done without leveraging the theoretical understanding between behavior and user traits that can be used to characterize individual users or the relationship between user traits and needs that can be used to adapt the system. Adopting a more theoretical perspective can benefit personalization in two ways: (i) letting systems rely on theory can reduce the need for extensive data-driven analysis, and (ii) interpreting the outcomes of datadriven analysis (such as predictive models) from a theoretical perspective can expand our knowledge about users. However, incorporating theoretical knowledge in personalization brings forth a number of challenges. In this chapter, we review literature that taps into aspects of (i) psychological models from traditional psychological theory that can be used in personalization, (ii) relationships between psychological models and online behavior, (iii) automated inference of psychological models from data, and (iv) how to incorporate psychological models in personalized systems. Finally, we propose a step-by-step approach on how to design personalized systems that take user traits into account.

Abstract

Personalized systems are systems that adapt themselves to meet the inferred needs of individual users. The majority of personalized systems mainly rely on data describing how users interacted with these systems. A common approach is to use historical data to predict users’ future needs, preferences, and behavior to subsequently adapt the system to these predictions. However, this adaptation is often done without leveraging the theoretical understanding between behavior and user traits that can be used to characterize individual users or the relationship between user traits and needs that can be used to adapt the system. Adopting a more theoretical perspective can benefit personalization in two ways: (i) letting systems rely on theory can reduce the need for extensive data-driven analysis, and (ii) interpreting the outcomes of datadriven analysis (such as predictive models) from a theoretical perspective can expand our knowledge about users. However, incorporating theoretical knowledge in personalization brings forth a number of challenges. In this chapter, we review literature that taps into aspects of (i) psychological models from traditional psychological theory that can be used in personalization, (ii) relationships between psychological models and online behavior, (iii) automated inference of psychological models from data, and (iv) how to incorporate psychological models in personalized systems. Finally, we propose a step-by-step approach on how to design personalized systems that take user traits into account.

Kapitel in diesem Buch

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of user modeling
  6. 1. Theory-grounded user modeling for personalized HCI 1
  7. 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
  8. Part II: User input and feedback
  9. 3. Automated personalization of input methods and processes 67
  10. 4. How to use socio-emotional signals for adaptive training 103
  11. 5. Explanations and user control in recommender systems 133
  12. Part III: Personalization approaches
  13. 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
  14. 7. Pictures as a tool for matching tourist preferences with destinations 183
  15. 8. Towards personalized virtual reality touring through cross-object user interfaces 201
  16. 9. User awareness in music recommender systems 223
  17. 10. Personalizing the user interface for people with disabilities 253
  18. 11. Adaptive workplace learning assistance 283
  19. Index 303
https://doi.org/10.1515/9783110552485-001

Kapitel in diesem Buch

  1. Frontmatter I
  2. Introduction V
  3. Contents IX
  4. List of Contributing Authors XI
  5. Part I: Foundations of user modeling
  6. 1. Theory-grounded user modeling for personalized HCI 1
  7. 2. Opportunities and challenges of utilizing personality traits for personalization in HCI 31
  8. Part II: User input and feedback
  9. 3. Automated personalization of input methods and processes 67
  10. 4. How to use socio-emotional signals for adaptive training 103
  11. 5. Explanations and user control in recommender systems 133
  12. Part III: Personalization approaches
  13. 6. Tourist trip recommendations – foundations, state of the art, and challenges 159
  14. 7. Pictures as a tool for matching tourist preferences with destinations 183
  15. 8. Towards personalized virtual reality touring through cross-object user interfaces 201
  16. 9. User awareness in music recommender systems 223
  17. 10. Personalizing the user interface for people with disabilities 253
  18. 11. Adaptive workplace learning assistance 283
  19. Index 303
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Heruntergeladen am 7.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/9783110552485-001/html
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