Personalized Human-Computer Interaction
Description
Personalized and adaptive systems employ user models to adapt content, services, interaction or navigation to individual users' needs. User models can be inferred from implicitly observed information, such as the user's interaction history or current location, or from explicitly entered information, such as user profile data or ratings. Applications of personalization include item recommendation, location-based services, learning assistance and the tailored selection of interaction modalities.
With the transition from desktop computers to mobile devices and ubiquitous environments, the need for adapting to changing contexts is even more important. However, this also poses new challenges concerning privacy issues, user control, transparency, and explainability. In addition, user experience and other human factors are becoming increasingly important.
This book describes foundations of user modeling, discusses user interaction as a basis for adaptivity, and showcases several personalization approaches in a variety of domains, including music recommendation, tourism, and accessible user interfaces.
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Persons
Mirjam Augstein a Professor for Personalized and Collaborative Systems at the University of Applied Sciences Upper Austria, as well as head of the research group on Personalized Environments and Collaborative Systems (PEEC). Her research interests are rooted in the broader field of HCI, focusing on personalization of interaction processes as well as interaction beyond the individual. Particularly, she investigates how users collaborate in co-located, remote and hybrid settings, how systems can provide support for such collaborations, and how the requirements of individuals and teams can be best met in all phases of design and development.
Mirjam regularly serves on the program committee of a broad range of scientific conferences in the field of HCI, e.g. recently as Hybrid Co-Chair for the ACM CSCW 2023 conference. Further, she was chair of ABIS between 2015 and 2022, and vice chair of the German ACM SIGCHI Chapter between 2019 and 2023.
Eelco Herder works as an Associate Professor in the Interaction Group at Utrecht University, the Netherlands. His research focuses on the fine balance between the benefits of personalization and perceived and actual risks associated with privacy matters. Particularly, he investigates how users and current (commercial) recommender systems respond to one another, and which mechanisms help to encourage users to actively choose what they want instead of passively following suggestions.
Since July 2019, he is Vice Chair of ACM SIGWEB. He is also a board member and information officer of User Modeling Inc. Between 2007 and 2015, he served as the chair of ABIS. Further, he was General Chair for ACM UMAP 2021 and as Program Chair for ACM Hypertext 2021.
Wolfgang Wörndl is a senior researcher and lecturer at the School of Computation, Information and Technology (CIT) at Technische Universitt Mnchen (TUM), Germany, working in the intersection between Human-Computer Interaction (HCI) and Artificial Intelligence (AI). His research focuses on human-centered and interactive recommender systems in mobile scenarios such as travel and tourism. Delivering personalized and timely recommendations is particularly valuable yet challenging in these domains. He currently investigates how humans can interact with these recommender systems, taking item combinations, multi-stakeholder issues and fairness into account.
Wolfgang has published over 100 refereed papers in relevant research areas. He is organizing and program committee member for a large number of journals, conferences and workshops, including co-chairing the ACM RecSys workshop series on Recommenders in Tourism. He served as vice chair for ABIS between 2015 and 2022, among other academic duties.
Content
- Intro
- Introduction
- Contents
- List of Contributing Authors
- Part I: Foundations of personalization
- 1 Theory-grounded user modeling for personalized HCI
- 2 User-centered recommender systems
- 3 Fairness of information access systems
- Part II: User input and feedback
- 4 Personalization and user modeling for interaction processes
- 5 How to use socio-emotional signals for adaptive training
- 6 Explanations and user control in recommender systems
- 7 Feedback loops and mutual reinforcement in personalized interaction
- Part III: Personalization purposes and goals
- 8 Personalizing the user interface for people with disabilities
- 9 Personalized persuasion for behavior change
- 10 Personalization approaches for remote collaborative interaction
- Part IV: Personalization domains
- 11 Listener awareness in music recommender systems: directions and current trends
- 12 Tourist trip recommendations - foundations, state of the art and challenges
- 13 Pictures as a tool for matching tourist preferences with destinations
- Index
