By Tsvi Kuflik, Shlomo Berkovsky, Francesca Carmagnola, Dominikus Heckmann, Antonio Krüger
Ubiquitous person modeling differs from favourite person modeling by way of 3 extra suggestions: ongoing modeling, ongoing sharing, and ongoing exploitation. structures that proportion their consumer versions will enhance the insurance, the extent of element, and the reliability of the built-in person versions and hence enable higher services of model. Ubiquitous consumer modeling implies new demanding situations of interchangeability, scalability, scrutability, and privacy.
This quantity provides result of a sequence of workshops with regards to Ubiquitous consumer Modeling on account that 2003 and extra workshops at a number of different meetings e.g. on consumer Modeling and Adaptive Hypermedia within the final 4 years.
The eight revised complete papers offered have been conscientiously reviewed and chosen from the simplest lectures given on the workshops and have been considerably prolonged to be incorporated within the book.
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Additional resources for Advances in Ubiquitous User Modelling: Revised Selected Papers
1014 . Below this score the terms should not be considered as semantically related, while above this score they can be assumed as related. The highest is the Osm score, the greatest is the semantic relation among the terms. 2 Property Similarity Algorithm As said in Section 4, the property of a RDF statement in the shareable user model relates an instance of the resource User with an Object. Assuming the user be the same among two statements belonging to P and R and the Osm Algorithm results that (object(sP )) and (object(s1R )) are similar.
The highest similarity measure between a provider’s statement sP and the receiver’s statement s1R gives the highest relevance of sP for the receiver R. The algorithms for similarity measure have been implemented in Java using Sesame and SeRQL and are described in more detail in . 6 Conclusion The paper has proposed our solution to manage the semantic heterogeneity of interoperable distributed user models in order to automate the user model exchange across user-adaptive applications. With respect to the works proposed in the community, our approach is not performed through the use of a shared format approach, nor exploiting algorithms to bootstrap user models in one system by using information from other systems.
Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide. AI Commun. 19(4), 369–384 (2006) 10. : Distributed user modelling for universal information access. Internation Journal of Human Compueter Interaction, 122–126 (2001) 11. : What is a conversation policy? , Greaves, M. ) Issues in Agent Communication. LNCS, vol. 1916, pp. 118–131. Springer, Heidelberg (2000) Handling Semantic Heterogeneity in Interoperable Distributed User Models 35 12. : Interoperability in personalized adaptive learning.