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

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

This booklet 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 specialize in pioneering theories, thoughts, 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

Sample text

A user can rate every post once, but only if there is free energy in the system available. The system provides a limited number of energy units, depending on the level of activity in the system. The number of energy units in the system increases every time when a new post is created (2 new units are added), and it decays with time. In this way, the scarcity of energy in the system prevents users from overrating their colleagues posts, and encourages them to carefully read a post before assigning energy to it.

The proposed protocol consists of two steps executed by the buyers and two steps executed by the sellers. • • • • Step1 (sellers listen): The sellers are tuned to the channels corresponding to the attributes of their objects and wait for queries indicating that the buyers are looking for products represented by pairs. For example, seller1 is tuned to the channels product, type, year, price and owners. Step 2 (buyers broadcast): The buyers send the desired values of the attributes they are searching.

Figure 2 displays the precision-recall distribution for the non-personalized baseline algorithm and the personalized recommendations based on users who have contributed at least 2, 3, 4, or 5 posts in the training set. While for k <= 3 the classes 3 to 5 perform better than class 2, class 2 performs better for k > 4. However, none of the different classes results in significantly better results than the other classes. 8 1 Recall Fig. 2. The precision-recall diagram based on implicit user feedback for users who have posted at least 2, 3, 4, or 5 times in the training set week of the threads the users have contributed to.

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