Feedback Loops and Mutual Reinforcement in Personalized Interaction
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.
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| Titel | Feedback Loops and Mutual Reinforcement in Personalized Interaction |
|---|---|
| Medien | Personalized Human-Computer Interaction |
| Verlag | De Gruyter |
| Heft | --- |
| Band | 2023 |
| ISBN | --- |
| Verfasser/Herausgeber | Eelco Herder, Prof. Dr. Claus Atzenbeck |
| Seiten | 153–172 |
| Veröffentlichungsdatum | 02.10.2023 |
| Projekttitel | --- |
| Zitation | Herder, Eelco; Atzenbeck, Claus (2023): Feedback Loops and Mutual Reinforcement in Personalized Interaction. Personalized Human-Computer Interaction 2023, 7, S. 153–172. DOI: 10.1515/9783110988567-007 |