research-article

Learning user preferences for adaptive pervasive environments: An incremental and temporal approach

Authors:

Sarah Gallacher,

Eliza Papadopoulou,

Nick K. Taylor,

M. Howard WilliamsAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 8, Issue 1

Article No.: 5, Pages 1 - 26

https://doi.org/10.1145/2451248.2451253

Published: 19 April 2013 Publication History

Abstract

Personalization mechanisms often employ behavior monitoring and machine learning techniques to aid the user in the creation and management of a preference set that is used to drive the adaptation of environments and resources in line with individual user needs. This article reviews several of the personalization solutions provided to date and proposes two hypotheses: (A) an incremental machine learning approach is better suited to the preference learning problem as opposed to the commonly employed batch learning techniques, (B) temporal data related to the duration that user context states and preference settings endure is a beneficial input to a preference learning solution. These two hypotheses are the cornerstones of the Dynamic Incremental Associative Neural NEtwork (DIANNE) developed as a tailored solution to preference learning in a pervasive environment. DIANNE has been evaluated in two ways: first, by applying it to benchmark datasets to test DIANNE's performance and scalability as a machine learning solution; second, by end-users in live trials to determine the validity of the proposed hypotheses and to evaluate DIANNE's utility as a preference learning solution.

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Cited By

Kuriakose BShrestha RSandnes F(2020)Multimodal Navigation Systems for Users with Visual Impairments—A Review and AnalysisMultimodal Technologies and Interaction10.3390/mti40400734:4(73)Online publication date: 16-Oct-2020
https://doi.org/10.3390/mti4040073
Tavcar JHorvath I(2019)A Review of the Principles of Designing Smart Cyber-Physical Systems for Run-Time Adaptation: Learned Lessons and Open IssuesIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2018.281453949:1(145-158)Online publication date: Jan-2019
https://doi.org/10.1109/TSMC.2018.2814539
Lim JSon HLee DLee D(2017)An MARL-Based Distributed Learning Scheme for Capturing User Preferences in a Smart Environment2017 IEEE International Conference on Services Computing (SCC)10.1109/SCC.2017.24(132-139)Online publication date: Jun-2017
https://doi.org/10.1109/SCC.2017.24
Show More Cited By

Index Terms

Learning user preferences for adaptive pervasive environments: An incremental and temporal approach
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 8, Issue 1

April 2013

126 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2451248

Issue’s Table of Contents

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 April 2013

Accepted: 01 August 2012

Revised: 01 April 2012

Received: 01 October 2011

Published in TAAS Volume 8, Issue 1

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Cited By

Kuriakose BShrestha RSandnes F(2020)Multimodal Navigation Systems for Users with Visual Impairments—A Review and AnalysisMultimodal Technologies and Interaction10.3390/mti40400734:4(73)Online publication date: 16-Oct-2020
https://doi.org/10.3390/mti4040073
Tavcar JHorvath I(2019)A Review of the Principles of Designing Smart Cyber-Physical Systems for Run-Time Adaptation: Learned Lessons and Open IssuesIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2018.281453949:1(145-158)Online publication date: Jan-2019
https://doi.org/10.1109/TSMC.2018.2814539
Lim JSon HLee DLee D(2017)An MARL-Based Distributed Learning Scheme for Capturing User Preferences in a Smart Environment2017 IEEE International Conference on Services Computing (SCC)10.1109/SCC.2017.24(132-139)Online publication date: Jun-2017
https://doi.org/10.1109/SCC.2017.24
Gasparetti F(2017)Modeling user interests from web browsing activitiesData Mining and Knowledge Discovery10.1007/s10618-016-0482-x31:2(502-547)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10618-016-0482-x
Hoque MKabir MSeo HYang S(2016)PARE: Profile-Applied Reasoning Engine for Context-Aware SystemInternational Journal of Distributed Sensor Networks10.1177/15501477538909112:7(5389091)Online publication date: 25-Jul-2016
https://doi.org/10.1177/155014775389091
Ye JFang LDobson SLukowicz PKrüger ABulling ALim YPatel S(2016)Discovery and recognition of unknown activitiesProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct10.1145/2968219.2968288(783-792)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2968219.2968288
García-galán JPasquale LTrinidad PRuiz-Cortés A(2016)User-Centric Adaptation Analysis of Multi-Tenant ServicesACM Transactions on Autonomous and Adaptive Systems10.1145/279030310:4(1-26)Online publication date: 13-Jan-2016
https://dl.acm.org/doi/10.1145/2790303
Karkouch AMousannif HAl Moatassime HNoel T(2016)Data quality in internet of thingsJournal of Network and Computer Applications10.1016/j.jnca.2016.08.00273:C(57-81)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.jnca.2016.08.002
Bental DPapadopoulou ETaylor NWilliams MBlackmun FIbrahim ILim MMimtsoudis IWhyte SJennings E(2015)Smartening Up the Student Learning Experience with Ubiquitous MediaACM Transactions on Multimedia Computing, Communications, and Applications10.1145/280820312:1s(1-23)Online publication date: 21-Oct-2015
https://dl.acm.org/doi/10.1145/2808203
Papadopoulou ETaylor NWilliams MGallacher S(2013)Learning user preferences in a system combining pervasive behaviour and social networking2013 8th International Conference on Information Technology in Asia (CITA)10.1109/CITA.2013.6637576(1-7)Online publication date: Jul-2013
https://doi.org/10.1109/CITA.2013.6637576

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