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This ebook constitutes the refereed court cases of the fifth foreign convention on Adaptive Hypermedia and Adaptive Web-Based platforms, AH 2008, held in Hannover, Germany in July 2008.
The 24 revised complete papers and 26 revised brief papers offered including abstracts of three keynotes, four demo papers, and eleven doctoral consortium posters have been rigorously reviewed and chosen from 185 papers submitted. The papers concentrate on pioneering theories, options, and cutting edge applied sciences to supply dynamic personalization, edition, and contextualization of hypermedia assets and services.
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Extra resources for Adaptive Hypermedia and Adaptive Web-Based Systems: 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings
Syst. 22(1), 5–53 (2004) 15. com/ ˇ 16. : Theoretical and empirical analysis of relieﬀ and rrelieﬀ. Mach. Learn. edu Abstract. The rapidly growing amount of heterogeneous semi-structured data available on the Web is creating a need for simple and universal access methods. For this purpose, we propose exploiting the notion of UNSpecified Ontology (UNSO), where the data objects are described using a list of attributes and their values. To facilitate efficient management of UNSO data objects, we use LoudVoice, a multi-agent channeled multicast communication platform, where each attribute is assigned a designated communication channel.
For eﬃciency reasons, for all the item selection algorithms, we computed oﬀline all the weights and later we used these stored values in the user-to-user similarity computation. In practice, to store all the weights, we need an M × M matrix of weights, where M is the cardinality of the item set. In other words, one vector of weights of size M is used for each item prediction. 4 Experimental Evaluation In this section we present the evaluation of BIPO item selection method for neighbor selection.
This issue is known as new item problem in collaborative recommender systems . Overall, the results imply that a) the non-personalized baseline algorithm is outperformed by the personalized algorithm and that b) two posts in a week are suﬃcient to generate precise personalized recommendations while more posts do not improve this the results signiﬁcantly. 2 Implicit vs. Explicit User Feedback Based on the classes deﬁned in the previous section which used implicit user feedback by engaging the posts a user created, we deﬁne equivalent classes of explicit user feedback: These classes contain users who have at least added or removed x energy points to posts from other users in the training set week and have at least posted once in the test set week.