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

ISBN-10: 3540709843

ISBN-13: 9783540709848

ISBN-10: 3540709878

ISBN-13: 9783540709879

This e-book constitutes the refereed complaints 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 awarded 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 specialize in pioneering theories, ideas, and leading edge applied sciences to supply dynamic personalization, version, 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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These result shows that both methods can predict a large common subset user-item pairs in the test data set, namely 17833. 5%. Moreover, BIPO can make more predictions compared to the baseline method (117 vs 1146 items not in the common subset). This result is surprising and shows that despite data sparsity, carefully selecting items for neighborhood formation increases recall and could also improve recommendation diversity. The increase in recall (and also in the ability to make more predictions) shown by BIPO can be explained by better analyzing the way user-to-user similarity is computed by this method.

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. In: SIGIR 2007: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp.

Mutual Information between two items is defined as : M = wji p(x, y) log x∈j y∈i p(x, y) p(x)p(y) and in practice it is implemented using the entropy as in [8]. Genre weighting. The previous methods exploit statistics of the users’ rating data to compute item weights. The last method we present here computes weights using description of the items. In the movie recommendation data set, which we are going to use for our experiments, movies are tagged with movie genres. Hence, we make the assumption that the larger the number of common tags, here genres, the higher is the dependency.

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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.)


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