Predicting user interface preferences of culturally ambiguous users

Authors:
Katharina Reinecke;Abraham Bernstein
Affiliations:
University of Zurich, Zurich, Switzerland;University of Zurich, Zurich, Switzerland
Venue:
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Year:
2008

Citing 4
Cited 4

Cultural user interfaces: a silver lining in cultural diversity

ACM SIGCHI Bulletin
Applying culture to website design: a comparison of Malaysian and US websites

IPCC/SIGDOC '00 Proceedings of IEEE professional communication society international professional communication conference and Proceedings of the 18th annual ACM international conference on Computer documentation: technology & teamwork
Adaptable and Adaptive User Interfaces for Disabled Users in the AVANTI Project

IS&N '98 Proceedings of the 5th International Conference on Intelligence and Services in Networks: Technology for Ubiquitous Telecom Services
Gumo: the general user model ontology

UM'05 Proceedings of the 10th international conference on User Modeling

Tell Me Where You've Lived, and I'll Tell You What You Like: Adapting Interfaces to Cultural Preferences

UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Combining ontologies and scenarios for context-aware e-learning environments

Proceedings of the 28th ACM International Conference on Design of Communication
Improving performance, perceived usability, and aesthetics with culturally adaptive user interfaces

ACM Transactions on Computer-Human Interaction (TOCHI)
Knowing what a user likes: a design science approach to interfaces that automatically adapt to culture

MIS Quarterly

Quantified Score

Hi-index	0.00

Visualization

Abstract

To date, localized user interfaces are still being adapted to one nation, not taking into account cultural ambiguities of people within this nation. We have developed an approach to cultural user modeling, which allows to personalize user interfaces to an individual's cultural background. The study presented in this paper shows how we use this approach to predict user interface preferences. Results show that we are able to reduce the absolute error on this prediction to 1.079 on a rating scale of 5. These findings suggest that it is possible to automate the process of localization and, thus, to automatically personalizing user interfaces for users of different cultural backgrounds.