Implicit adaptation of user preferences in pervasive systems

Sarah McBurney; Elizabeth Papadopoulou; Nick Taylor; Howard Williams

doi:10.1109/ICONS.2009.19

Implicit adaptation of user preferences in pervasive systems

Sarah McBurney, Elizabeth Papadopoulou, Nick Taylor, Howard Williams

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

6 Citations (Scopus)

Abstract

User preferences have an essential role to play in decision making in pervasive systems. However, building up and maintaining a set of user preferences for an individual user is a nontrivial exercise. Relying on the user to input preferences has been found not to work and the use of different forms of machine learning are being investigated. This paper is concerned with the problem of updating a set of preferences when a new aspect of an existing preference is discovered. A basic algorithm (with variants) is given for handling this situation. This has been developed for the Daidalos and Persist pervasive systems. Some research issues are also discussed. © 2009 IEEE.

Original language	English
Title of host publication	Proceedings of the 4th International Conference on Systems, ICONS 2009
Pages	56-62
Number of pages	7
DOIs	https://doi.org/10.1109/ICONS.2009.19
Publication status	Published - 2009
Event	4th International Conference on Systems - Gosier, Guadeloupe Duration: 1 Mar 2009 → 6 Mar 2009

Conference

Conference	4th International Conference on Systems
Abbreviated title	ICONS 2009
Country/Territory	Guadeloupe
City	Gosier
Period	1/03/09 → 6/03/09

Keywords

Machine learning
Pervasive systems
User preferences

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10.1109/ICONS.2009.19

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title = "Implicit adaptation of user preferences in pervasive systems",

abstract = "User preferences have an essential role to play in decision making in pervasive systems. However, building up and maintaining a set of user preferences for an individual user is a nontrivial exercise. Relying on the user to input preferences has been found not to work and the use of different forms of machine learning are being investigated. This paper is concerned with the problem of updating a set of preferences when a new aspect of an existing preference is discovered. A basic algorithm (with variants) is given for handling this situation. This has been developed for the Daidalos and Persist pervasive systems. Some research issues are also discussed. {\textcopyright} 2009 IEEE.",

keywords = "Machine learning, Pervasive systems, User preferences",

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

year = "2009",

doi = "10.1109/ICONS.2009.19",

language = "English",

isbn = "9780769535517",

pages = "56--62",

booktitle = "Proceedings of the 4th International Conference on Systems, ICONS 2009",

note = "4th International Conference on Systems, ICONS 2009 ; Conference date: 01-03-2009 Through 06-03-2009",

}

TY - GEN

T1 - Implicit adaptation of user preferences in pervasive systems

AU - McBurney, Sarah

AU - Papadopoulou, Elizabeth

AU - Taylor, Nick

AU - Williams, Howard

PY - 2009

Y1 - 2009

N2 - User preferences have an essential role to play in decision making in pervasive systems. However, building up and maintaining a set of user preferences for an individual user is a nontrivial exercise. Relying on the user to input preferences has been found not to work and the use of different forms of machine learning are being investigated. This paper is concerned with the problem of updating a set of preferences when a new aspect of an existing preference is discovered. A basic algorithm (with variants) is given for handling this situation. This has been developed for the Daidalos and Persist pervasive systems. Some research issues are also discussed. © 2009 IEEE.

AB - User preferences have an essential role to play in decision making in pervasive systems. However, building up and maintaining a set of user preferences for an individual user is a nontrivial exercise. Relying on the user to input preferences has been found not to work and the use of different forms of machine learning are being investigated. This paper is concerned with the problem of updating a set of preferences when a new aspect of an existing preference is discovered. A basic algorithm (with variants) is given for handling this situation. This has been developed for the Daidalos and Persist pervasive systems. Some research issues are also discussed. © 2009 IEEE.

KW - Machine learning

KW - Pervasive systems

KW - User preferences

U2 - 10.1109/ICONS.2009.19

DO - 10.1109/ICONS.2009.19

M3 - Conference contribution

SN - 9780769535517

SP - 56

EP - 62

BT - Proceedings of the 4th International Conference on Systems, ICONS 2009

T2 - 4th International Conference on Systems

Y2 - 1 March 2009 through 6 March 2009

ER -

Implicit adaptation of user preferences in pervasive systems

Abstract

Conference

Keywords

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