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Generating Personalized Destination Suggestions for Automotive Navigation Systems under Uncertainty

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45609

Theobald,  Martin
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45025

Meiser,  Timm
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Feld, M., Theobald, M., Stahl, C., Meiser, T., & Müller, C. (2011). Generating Personalized Destination Suggestions for Automotive Navigation Systems under Uncertainty. In F. Abel, S. M. Baldiris, & N. Henze (Eds.), Adjunct Proceedings of the 19th International Conference on User Modeling, Adaptation and Personalization (pp. 22-24). s.l.: UMAP 2011. Retrieved from http://www.umap2011.org/proceedings/posters/paper_231.pdf.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-1462-C
Zusammenfassung
Programming a car's navigation system manually takes time and is error-prone. When the address is not handy, a cumbersome search may start. Changing the destination while driving is even more problematic. Given a modern car's role as an information hub, we argue that an intelligent system could in many cases infer the right destination or have it among the top N suggestions. In this work, we propose a personalized navigation system that is built from three main ingredients: strong user models, knowledge source fusion, and reasoning under uncertainty. We focus on emails as one particular knowledge source, exploring the uncertainties involved when extracting empirical data of email appointments.