Context-aware web content adaptation for mobile user agents

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

5 Citations (Scopus)

Abstract

There is a growing need for mobile users to access Web content. In this chapter, adaptation approaches and techniques are described that lets mobile users access Web content that’s not directly targeted to user agents of mobile devices. Content adaptation can be based on the information of delivery context, which can include static or dynamic information including device capabilities, network characteristics, user preferences, and other application-specific parameters. Content selection may allow selection between versions of the material. Web content can be adapted as is or on the basis of author-provided or any additional metadata related to the input. Content authors may choose among several authoring methods related to adaptation. This chapter also outlines typical use cases of Web content adaptation
Original languageEnglish
Title of host publicationEvolution of the Web in Artificial Intelligence Environments
EditorsRichi Nayak, N. Ichalkaranje
PublisherSpringer
Chapter4
Pages69-99
ISBN (Electronic)978-3-540-79140-9
ISBN (Print)978-3-540-79139-3, 978-3-642-09802-4
Publication statusPublished - 2008
MoE publication typeA3 Part of a book or another research book

Publication series

NameStudies in Computational Intelligence
PublisherSpringer
Volume130
ISSN (Print)1860-949X

Fingerprint

Metadata
Mobile devices

Cite this

Laakko, T. (2008). Context-aware web content adaptation for mobile user agents. In R. Nayak, & N. Ichalkaranje (Eds.), Evolution of the Web in Artificial Intelligence Environments (pp. 69-99). Springer. Studies in Computational Intelligence, Vol.. 130
Laakko, Timo. / Context-aware web content adaptation for mobile user agents. Evolution of the Web in Artificial Intelligence Environments. editor / Richi Nayak ; N. Ichalkaranje. Springer, 2008. pp. 69-99 (Studies in Computational Intelligence, Vol. 130).
@inbook{b994777534a54ef6b54a110b9a2a8905,
title = "Context-aware web content adaptation for mobile user agents",
abstract = "There is a growing need for mobile users to access Web content. In this chapter, adaptation approaches and techniques are described that lets mobile users access Web content that’s not directly targeted to user agents of mobile devices. Content adaptation can be based on the information of delivery context, which can include static or dynamic information including device capabilities, network characteristics, user preferences, and other application-specific parameters. Content selection may allow selection between versions of the material. Web content can be adapted as is or on the basis of author-provided or any additional metadata related to the input. Content authors may choose among several authoring methods related to adaptation. This chapter also outlines typical use cases of Web content adaptation",
author = "Timo Laakko",
note = "CA2: TK706",
year = "2008",
language = "English",
isbn = "978-3-540-79139-3",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "69--99",
editor = "Richi Nayak and N. Ichalkaranje",
booktitle = "Evolution of the Web in Artificial Intelligence Environments",
address = "Germany",

}

Laakko, T 2008, Context-aware web content adaptation for mobile user agents. in R Nayak & N Ichalkaranje (eds), Evolution of the Web in Artificial Intelligence Environments. Springer, Studies in Computational Intelligence, vol. 130, pp. 69-99.

Context-aware web content adaptation for mobile user agents. / Laakko, Timo.

Evolution of the Web in Artificial Intelligence Environments. ed. / Richi Nayak; N. Ichalkaranje. Springer, 2008. p. 69-99 (Studies in Computational Intelligence, Vol. 130).

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

TY - CHAP

T1 - Context-aware web content adaptation for mobile user agents

AU - Laakko, Timo

N1 - CA2: TK706

PY - 2008

Y1 - 2008

N2 - There is a growing need for mobile users to access Web content. In this chapter, adaptation approaches and techniques are described that lets mobile users access Web content that’s not directly targeted to user agents of mobile devices. Content adaptation can be based on the information of delivery context, which can include static or dynamic information including device capabilities, network characteristics, user preferences, and other application-specific parameters. Content selection may allow selection between versions of the material. Web content can be adapted as is or on the basis of author-provided or any additional metadata related to the input. Content authors may choose among several authoring methods related to adaptation. This chapter also outlines typical use cases of Web content adaptation

AB - There is a growing need for mobile users to access Web content. In this chapter, adaptation approaches and techniques are described that lets mobile users access Web content that’s not directly targeted to user agents of mobile devices. Content adaptation can be based on the information of delivery context, which can include static or dynamic information including device capabilities, network characteristics, user preferences, and other application-specific parameters. Content selection may allow selection between versions of the material. Web content can be adapted as is or on the basis of author-provided or any additional metadata related to the input. Content authors may choose among several authoring methods related to adaptation. This chapter also outlines typical use cases of Web content adaptation

M3 - Chapter or book article

SN - 978-3-540-79139-3

SN - 978-3-642-09802-4

T3 - Studies in Computational Intelligence

SP - 69

EP - 99

BT - Evolution of the Web in Artificial Intelligence Environments

A2 - Nayak, Richi

A2 - Ichalkaranje, N.

PB - Springer

ER -

Laakko T. Context-aware web content adaptation for mobile user agents. In Nayak R, Ichalkaranje N, editors, Evolution of the Web in Artificial Intelligence Environments. Springer. 2008. p. 69-99. (Studies in Computational Intelligence, Vol. 130).