Advances in Ubiquitous User Modelling
Revised Selected Papers
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Beschreibung
Ubiquitous user modeling differs from generic user modeling by three additional concepts: ongoing modeling, ongoing sharing, and ongoing exploitation. Systems that share their user models will improve the coverage, the level of detail, and the reliability of the integrated user models and thus allow better functions of adaptation. Ubiquitous user modeling implies new challenges of interchangeability, scalability, scrutability, and privacy.
This volume presents results of a series of workshops on the topic of Ubiquitous User Modeling since 2003 and additional workshops at various other conferences e.g. on User Modeling and Adaptive Hypermedia in the last four years.
The 8 revised full papers presented were carefully reviewed and selected from the best lectures given at the workshops and were significantly extended to be included in the book.
Ubiquitous user modeling differs from generic user modeling by three additional concepts: ongoing modeling, ongoing sharing, and ongoing exploitation. Systems that share their user models will improve the coverage, the level of detail, and the reliability of the integrated user models and thus allow better functions of adaptation. Ubiquitous user modeling implies new challenges of interchangeability, scalability, scrutability, and privacy. This volume presents results of a series of workshops on the topic of Ubiquitous User Modeling since 2003 and additional workshops at various other conferences e.g. on User Modeling and Adaptive Hypermedia in the last four years. The 8 revised full papers presented were carefully reviewed and selected from the best lectures given at the workshops and were significantly extended to be included in the book.
Autor*in
Tsvi Kuflik
Themen in »Advances in Ubiquitous User Modelling«
HCI adaptive hypermedia adaptive systems authoring cognitive science collaborative techniques context awareness data mining e-learning faceted browsing human-centered design human-computer interaction inference agents modeling privacy