7 Feedback loops and mutual reinforcement in personalized interaction
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Eelco Herder
Abstract
In personalized interaction between humans and computers, not only computers and personalization algorithms learn about the users: the users also learn about the system’s behavior and adapt their expectations accordingly. Particularly, as users expect systems to support their daily activities, this feedback loop may result in long-term changes in these daily activities and user decisions themselves. This can be observed in activities as different as autonomous driving and social media consumption. In this chapter, we investigate these effects by reviewing and analyzing a wide range of relevant literature.
Abstract
In personalized interaction between humans and computers, not only computers and personalization algorithms learn about the users: the users also learn about the system’s behavior and adapt their expectations accordingly. Particularly, as users expect systems to support their daily activities, this feedback loop may result in long-term changes in these daily activities and user decisions themselves. This can be observed in activities as different as autonomous driving and social media consumption. In this chapter, we investigate these effects by reviewing and analyzing a wide range of relevant literature.
Chapters in this book
- 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
Chapters in this book
- 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
