1 Theory-grounded user modeling for personalized HCI
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Mark P. Graus
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 cater 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 three ways: (i) relying on theory can reduce the amount of data required to train compared to a purely data-driven system, (ii) interpreting the outcomes of data-driven analysis (such as predictive models) from a theoretical perspective can expand our knowledge about users and (iii) provide means for explanations and transparency. However, in order to incorporate theoretical knowledge in personalization a number of obstacles need to be faced. 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 users’ 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 cater 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 three ways: (i) relying on theory can reduce the amount of data required to train compared to a purely data-driven system, (ii) interpreting the outcomes of data-driven analysis (such as predictive models) from a theoretical perspective can expand our knowledge about users and (iii) provide means for explanations and transparency. However, in order to incorporate theoretical knowledge in personalization a number of obstacles need to be faced. 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 users’ traits into account.
Kapitel in diesem Buch
- Frontmatter I
- Introduction V
- Contents IX
- List of Contributing Authors XI
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Part I: Foundations of personalization
- 1 Theory-grounded user modeling for personalized HCI 1
- 2 User-centered recommender systems 33
- 3 Fairness of information access systems 59
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Part II: User input and feedback
- 4 Personalization and user modeling for interaction processes 81
- 5 How to use socio-emotional signals for adaptive training 99
- 6 Explanations and user control in recommender systems 129
- 7 Feedback loops and mutual reinforcement in personalized interaction 153
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Part III: Personalization purposes and goals
- 8 Personalizing the user interface for people with disabilities 175
- 9 Personalized persuasion for behavior change 205
- 10 Personalization approaches for remote collaborative interaction 237
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Part IV: Personalization domains
- 11 Listener awareness in music recommender systems: directions and current trends 279
- 12 Tourist trip recommendations – foundations, state of the art and challenges 313
- 13 Pictures as a tool for matching tourist preferences with destinations 337
- Index 355
Kapitel in diesem Buch
- Frontmatter I
- Introduction V
- Contents IX
- List of Contributing Authors XI
-
Part I: Foundations of personalization
- 1 Theory-grounded user modeling for personalized HCI 1
- 2 User-centered recommender systems 33
- 3 Fairness of information access systems 59
-
Part II: User input and feedback
- 4 Personalization and user modeling for interaction processes 81
- 5 How to use socio-emotional signals for adaptive training 99
- 6 Explanations and user control in recommender systems 129
- 7 Feedback loops and mutual reinforcement in personalized interaction 153
-
Part III: Personalization purposes and goals
- 8 Personalizing the user interface for people with disabilities 175
- 9 Personalized persuasion for behavior change 205
- 10 Personalization approaches for remote collaborative interaction 237
-
Part IV: Personalization domains
- 11 Listener awareness in music recommender systems: directions and current trends 279
- 12 Tourist trip recommendations – foundations, state of the art and challenges 313
- 13 Pictures as a tool for matching tourist preferences with destinations 337
- Index 355
