September 2, 2017

Download Adaptive Hypermedia and Adaptive Web-Based Systems: 4th by Sean Bechhofer, Yeliz Yesilada, Bernard Horan, Carole Goble PDF

By Sean Bechhofer, Yeliz Yesilada, Bernard Horan, Carole Goble (auth.), Vincent P. Wade, Helen Ashman, Barry Smyth (eds.)

ISBN-10: 3540346961

ISBN-13: 9783540346968

This booklet constitutes the refereed complaints of the 4th overseas convention on Adaptive Hypermedia and Adaptive Web-Based structures, AH 2006, held in Dublin, eire in June 2006.

The 22 revised complete papers and 19 revised brief papers provided including abstracts of three keynotes, 12 poster papers, and 14 doctoral consortium posters have been conscientiously reviewed and chosen from 122 papers submitted. The papers concentrate on pioneering theories, recommendations, and cutting 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: 4th International Conference, AH 2006, Dublin, Ireland, June 21-23, 2006. Proceedings

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García, and J. Carabias creation of adaptive rules based on the IMS LD element and its subelements using the following formalism (BNF notation): ::= IF THEN (1) ::= [ | ] (2) ::= [ | ] (3) ::= [ | | | | | | | | | |] ::= [ | | ] ::= [ | | | ] (4) (5) (6) Using this formalism, the expression-builder tool guides authors in the definition of adaptive rules.

It initially decreases with min-occurs, filtering the noisy features, and then monotonically increases, as for a higher min-occurs threshold, also important features with a high number of occurrences are being filtered. As for the prediction rate, it monotonically decreases with min-occurs. Similarly to the confidence threshold, this is explained by the fact that the high threshold filters important features, and the prediction generations are harder. 28 S. Berkovsky, T. Kuflik, and F. 2 0 0 0% 12% 24% 36% 48% 60% 0% 8% 16% 24% 32% 40% Fig.

The examples are mainly drawn from collaborative work I’ve been involved with over the last decade, on a series of AH systems using NLP: ILEX, SOLE, M-PIRO and Methodius. In theory, NLP sub-systems should help find, filter and format information for representation in AH systems. So there ought to be lots of cross-fertilisation between NLP and AH. It is true that some projects have effectively brought them together; particularly on the formatting—or information presentation—side, natural language generation systems have allowed quite fine-grained personalisation of information to the language, interests and history of individual users.

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