By Jan Borchers (auth.), Wolfgang Nejdl, Judy Kay, Pearl Pu, Eelco Herder (eds.)
This publication constitutes the refereed complaints of the fifth foreign convention on Adaptive Hypermedia and Adaptive Web-Based structures, AH 2008, held in Hannover, Germany in July 2008.
The 24 revised complete papers and 26 revised brief papers awarded including abstracts of three keynotes, four demo papers, and eleven doctoral consortium posters have been conscientiously reviewed and chosen from 185 papers submitted. The papers concentrate on pioneering theories, thoughts, and leading edge applied sciences to supply dynamic personalization, version, and contextualization of hypermedia assets and services.
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Additional resources for Adaptive Hypermedia and Adaptive Web-Based Systems: 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings
Ricci neighbors of the target user. This may decrease the average correlation with the neighbor users but it computes correlations with higher reliability. Moreover, since the selection of items is made by analyzing both the target user and the neighbor proﬁle, the neighbors found are more diverse, as they can be correlated on diﬀerent subsets of items. This is very important and is explaining the increase in recall as the increased diversity of the neighbors increases the chance that a neighbor rated the target item.
User proﬁles or data provided in the Web DS. Each data source represents an encapsulated personalization algorithm like a collaborative recommender system SWS (Web Services). Each data source can be accessed as Web Service. Comtella Application. It represents the Comtella application (more details are described in the next section) Rule-based Recommendation Interface. This interface is used to specify personalization rules. 5 gives an example of such a rule. SWRL (Semantic Web Rule Language). It is used to specify the conversion of information between the Comtella-D application and the data sources.
Afterwards, by evaluating diﬀerent recommender strategies, we deﬁne a ﬂexible personalization rule in Section 4 that performs best in creating recommendation in diﬀerent scenarios. Section 5 contains related work and Section 6 gives a conclusion and some further ideas to be exploited. 2 Rule-Based Personalization System Personalization techniques have been investigated extensively in diﬀerent areas of computer science. Especially in the domains of recommender systems  and adaptive hypermedia , personalization algorithms have been developed and deployed in various systems.
Adaptive Hypermedia and Adaptive Web-Based Systems: 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings by Jan Borchers (auth.), Wolfgang Nejdl, Judy Kay, Pearl Pu, Eelco Herder (eds.)