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 booklet constitutes the refereed lawsuits 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 conscientiously reviewed and chosen from 185 papers submitted. The papers specialise in pioneering theories, recommendations, and leading edge applied sciences to supply dynamic personalization, variation, 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

Example text

Section 2 discusses the related work on the item selection. 1 we describe the item weighting methods that were used to determine importance of the item. The proposed locally adaptive item selection method is evaluated in Section 4. Finally, Section 5 draws the conclusions and presents future work. 2 Related Work Item selection techniques are well know tools used in Machine Learning (ML). In ML they are called feature selection and are widely used to improve the prediction accuracy of supervised classification [2,3].

Springer, Heidelberg (2007) 2. : A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11(1-5), 273–314 (1997) 3. : Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997) 4. : Query chains: learning to rank from implicit feedback. In: KDD 2005: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 239–248. ACM Press, New York (2005) 5. : Feature selection for ranking.

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. To recommend posts by using user ratings we modified the similarity function of the recommender system.

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