Adapting pervasive environments through machine learning and dynamic personalization

Sarah McBurney; Eliza Papadopoulou; Nick Taylor; Howard Williams

doi:10.1109/ISPA.2008.63

Adapting pervasive environments through machine learning and dynamic personalization

Sarah McBurney, Eliza Papadopoulou, Nick Taylor, Howard Williams

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Citations (Scopus)

Abstract

Current pervasive environments should contain mechanisms, such as personalization, that adapt the environment to help the user meet their individual needs. However, manually creating, maintaining and utilizing a preference set is no easy task for a user, requiring continued time and effort. A more desirable approach is to implicitly build and maintain the preference set by using monitoring and learning mechanisms and apply such preferences when required on behalf of the user. This paper introduces the Daidalos Personalization and Learning system which monitors user behaviour and context to not only build and maintain dynamic preferences but also to apply them in a dynamic fashion. An example scenario is presented to demonstrate how such mechanisms are used to adapt a pervasive environment on a user's behalf. © 2008 Crown Copyright.

Original language	English
Title of host publication	Proceedings of the 2008 International Symposium on Parallel and Distributed Processing with Applications, ISPA 2008
Pages	395-402
Number of pages	8
DOIs	https://doi.org/10.1109/ISPA.2008.63
Publication status	Published - 2008
Event	2008 IEEE/WIC/ACM International Conference on Web Intelligence - Sydney, NSW, Australia Duration: 9 Dec 2008 → 12 Dec 2008

Conference

Conference	2008 IEEE/WIC/ACM International Conference on Web Intelligence
Abbreviated title	WI 2008
Country/Territory	Australia
City	Sydney, NSW
Period	9/12/08 → 12/12/08

Keywords

Dynamic
Learning
Personalization
Pervasive
Preferences

Access to Document

10.1109/ISPA.2008.63

Cite this

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title = "Adapting pervasive environments through machine learning and dynamic personalization",

abstract = "Current pervasive environments should contain mechanisms, such as personalization, that adapt the environment to help the user meet their individual needs. However, manually creating, maintaining and utilizing a preference set is no easy task for a user, requiring continued time and effort. A more desirable approach is to implicitly build and maintain the preference set by using monitoring and learning mechanisms and apply such preferences when required on behalf of the user. This paper introduces the Daidalos Personalization and Learning system which monitors user behaviour and context to not only build and maintain dynamic preferences but also to apply them in a dynamic fashion. An example scenario is presented to demonstrate how such mechanisms are used to adapt a pervasive environment on a user's behalf. {\textcopyright} 2008 Crown Copyright.",

keywords = "Dynamic, Learning, Personalization, Pervasive, Preferences",

author = "Sarah McBurney and Eliza Papadopoulou and Nick Taylor and Howard Williams",

year = "2008",

doi = "10.1109/ISPA.2008.63",

language = "English",

isbn = "9780769534718",

pages = "395--402",

booktitle = "Proceedings of the 2008 International Symposium on Parallel and Distributed Processing with Applications, ISPA 2008",

note = "2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 ; Conference date: 09-12-2008 Through 12-12-2008",

}

McBurney, S, Papadopoulou, E, Taylor, N & Williams, H 2008, Adapting pervasive environments through machine learning and dynamic personalization. in Proceedings of the 2008 International Symposium on Parallel and Distributed Processing with Applications, ISPA 2008. pp. 395-402, 2008 IEEE/WIC/ACM International Conference on Web Intelligence, Sydney, NSW, Australia, 9/12/08. https://doi.org/10.1109/ISPA.2008.63

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AU - Papadopoulou, Eliza

AU - Taylor, Nick

AU - Williams, Howard

PY - 2008

Y1 - 2008

N2 - Current pervasive environments should contain mechanisms, such as personalization, that adapt the environment to help the user meet their individual needs. However, manually creating, maintaining and utilizing a preference set is no easy task for a user, requiring continued time and effort. A more desirable approach is to implicitly build and maintain the preference set by using monitoring and learning mechanisms and apply such preferences when required on behalf of the user. This paper introduces the Daidalos Personalization and Learning system which monitors user behaviour and context to not only build and maintain dynamic preferences but also to apply them in a dynamic fashion. An example scenario is presented to demonstrate how such mechanisms are used to adapt a pervasive environment on a user's behalf. © 2008 Crown Copyright.

AB - Current pervasive environments should contain mechanisms, such as personalization, that adapt the environment to help the user meet their individual needs. However, manually creating, maintaining and utilizing a preference set is no easy task for a user, requiring continued time and effort. A more desirable approach is to implicitly build and maintain the preference set by using monitoring and learning mechanisms and apply such preferences when required on behalf of the user. This paper introduces the Daidalos Personalization and Learning system which monitors user behaviour and context to not only build and maintain dynamic preferences but also to apply them in a dynamic fashion. An example scenario is presented to demonstrate how such mechanisms are used to adapt a pervasive environment on a user's behalf. © 2008 Crown Copyright.

KW - Dynamic

KW - Learning

KW - Personalization

KW - Pervasive

KW - Preferences

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U2 - 10.1109/ISPA.2008.63

DO - 10.1109/ISPA.2008.63

M3 - Conference contribution

SN - 9780769534718

SP - 395

EP - 402

BT - Proceedings of the 2008 International Symposium on Parallel and Distributed Processing with Applications, ISPA 2008

T2 - 2008 IEEE/WIC/ACM International Conference on Web Intelligence

Y2 - 9 December 2008 through 12 December 2008

ER -

Adapting pervasive environments through machine learning and dynamic personalization

Abstract

Conference

Keywords

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