Download Adaptive Hypermedia and Adaptive Web-Based Systems: 5th by Jan Borchers (auth.), Wolfgang Nejdl, Judy Kay, Pearl Pu, PDF

By Jan Borchers (auth.), Wolfgang Nejdl, Judy Kay, Pearl Pu, Eelco Herder (eds.)

This publication constitutes the refereed court cases of the fifth overseas 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 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, ideas, and cutting edge applied sciences to supply dynamic personalization, model, and contextualization of hypermedia assets and services.

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Extra info for Adaptive Hypermedia and Adaptive Web-Based Systems: 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings

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Data integration [3] and schema matching [8] research studies aim at addressing this challenge by, respectively, reconciling the ontologies and matching between the concepts pertaining to different ontologies. A global ontology allows a uniform data access mechanism and defines rules encapsulating the differences between the ontologies. Despite their relative success, neither data integration nor ontology matching have fully succeeded in resolving the issues related to the dynamic and variable nature of the ontologies.

3 Item Selection for Collaborative Filtering Finding the optimal subset of items would require to conduct an extensive search in the space of all the subsets of the items [3]. Applying this to a recommender system scenario would require to conduct a search procedure for every target item (the item playing the role of the class to be predicted) and for a large number of subsets of the predictive items. This is clearly extremely expensive, and therefore, we propose to use a more parsimonious approach (filter method) that uses information provided by a item weighting method to select, for each target item and user pair, an appropriate set of predictive items.

1. In figure 1 the performance of BIPO with all these methods is depicted. , collaborative filtering without item selection, for all the error measures used. 43 20 40 60 80 100 # items selected (c) PRECISION 120 140 20 40 60 80 100 # items selected 120 140 (d) RECALL Fig. 1. Performance of BIPO item selection methods for different error measures 28 L. Baltrunas and F. Ricci shows the robustness of item selection, and clearly shows that the improvements are due to item selection rather than weighting.

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