By Sean Bechhofer, Yeliz Yesilada, Bernard Horan, Carole Goble (auth.), Vincent P. Wade, Helen Ashman, Barry Smyth (eds.)
This ebook constitutes the refereed complaints of the 4th overseas convention on Adaptive Hypermedia and Adaptive Web-Based platforms, AH 2006, held in Dublin, eire in June 2006.
The 22 revised complete papers and 19 revised brief papers offered including abstracts of three keynotes, 12 poster papers, and 14 doctoral consortium posters have been rigorously reviewed and chosen from 122 papers submitted. The papers concentrate on pioneering theories, suggestions, and cutting edge applied sciences to supply dynamic personalization, variation, and contextualization of hypermedia assets and services.
Read or Download Adaptive Hypermedia and Adaptive Web-Based Systems: 4th International Conference, AH 2006, Dublin, Ireland, June 21-23, 2006. Proceedings PDF
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Additional resources for Adaptive Hypermedia and Adaptive Web-Based Systems: 4th International Conference, AH 2006, Dublin, Ireland, June 21-23, 2006. Proceedings
We believe this will significantly improve the accuracy of the personalization provided and strengthen the proposed cross-technique mediation. , the reverse translation, from content-based to collaborative UMs) and in different application domains. Cohen, “Recommendation as Classification: Using Social and Content-Based Information in Recommendation”, in proc. of the AAAI Conference, 1998. Berkovsky, “Decentralized Mediation of User Models for a Better Personalization”, in proc. of the AH Conference, 2006.
Adaptive Hypertext 2002. Springer (2002) 203 - 212 5. : Consistent Modelling of Users, Devices and Sensors in a Ubiquitous Computing Environment. User Modeling and User-Adapted Interaction 15 (2005) 197-234 6. : Verified Concept Mapping for Eliciting Conceptual Understanding. , Dicheva, D. ): ICCE Workshop on Concepts and Ontologies in Webbased Educational Systems, ICCE 2002, International Conference on Computers in Education. CS-Report 02-15 Technische Universiteit Eindhoven (2002) 9-14 7. : Exploiting readily available web data for scrutable student models.
1. MAE and prediction rate vs. confidence threshold the prediction mechanism and the MAE is higher. When the confidence increases, neutral features are filtered and MAE decreases. However, high values of the confidence filter also differentiating features, and MAE increases again. 99). Prediction rate monotonically decreases with confidence, since when more features are filtered, the task of generating a prediction is harder to accomplish. Note the difference between the experiments including and excluding the keywords features in prediction generation.