research-article

Employing clustering algorithms to create user groups for personalized context aware services provision

Authors:

Athanasios S. Voulodimos,

Charalampos Z. Patrikakis,

Pantelis N. Karamolegkos,

Anastasios D. Doulamis,

Emmanuel S. SardisAuthors Info & Claims

SBNMA '11: Proceedings of the 2011 ACM workshop on Social and behavioural networked media access

Pages 33 - 38

https://doi.org/10.1145/2072627.2072637

Published: 01 December 2011 Publication History

Abstract

The successful provision of context aware services entails the attainment of equilibrium between the extent of personalization desired and the user's need for privacy. Two are the major elements that play a significant role: the user's location and the user's preferences. In this paper we focus on the latter, and propose to employ a social groups' creation methodology, so as to hierarchically organize the user preferences concerning any domain in different levels of detail. We describe some notions and metrics which play a key role in social networking frameworks, and we perform an evaluation study of three widely used clustering methods (k-means, hierarchical and spectral clustering) in the scope of social groups assessment and in regard to the cardinality of the profile used to assess users' preferences. The results of the work can be used in many applications, including personalized media delivery, offering a framework on which next generation multimedia access can be provided.

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Cited By

Markoulidakis IRallis IGeorgoulas IKopsiaftis GDoulamis ADoulamis N(2020)A Machine Learning Based Classification Method for Customer Experience Survey AnalysisTechnologies10.3390/technologies80400768:4(76)Online publication date: 7-Dec-2020
https://doi.org/10.3390/technologies8040076
Doulamis AVoulodimos AProtopapadakis EDoulamis NMakantasis K(2020)Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter ImagesSustainability10.3390/su1210422312:10(4223)Online publication date: 21-May-2020
https://doi.org/10.3390/su12104223
Rallis IMarkoulidakis IGeorgoulas IKopsiaftis GMakedon F(2020)A novel classification method for customer experience survey analysisProceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3389189.3397999(1-9)Online publication date: 30-Jun-2020
https://dl.acm.org/doi/10.1145/3389189.3397999
Show More Cited By

Index Terms

Employing clustering algorithms to create user groups for personalized context aware services provision
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published In

cover image ACM Conferences

SBNMA '11: Proceedings of the 2011 ACM workshop on Social and behavioural networked media access

December 2011

78 pages

ISBN:9781450309905

DOI:10.1145/2072627

General Chairs:
Naeem Ramzan
Queen Mary, University London, UK
,
Fei Wang
IBM T.J.Watson Research Center, USA
,
Charalampos Z. Patrikakis
National Technical University of Athens, Greece
,
Peng Cui
Tsinghua University, China
,
Nikos Doulamis
National Technical University of Athens, Greece
,
Shiqiang Yang
Tsingua University, China
,
Gordon Sun
Tencent Technologies, China

Copyright © 2011 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2011

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MM '11: ACM Multimedia Conference

December 1, 2011

Arizona, Scottsdale, USA

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Cited By

Markoulidakis IRallis IGeorgoulas IKopsiaftis GDoulamis ADoulamis N(2020)A Machine Learning Based Classification Method for Customer Experience Survey AnalysisTechnologies10.3390/technologies80400768:4(76)Online publication date: 7-Dec-2020
https://doi.org/10.3390/technologies8040076
Doulamis AVoulodimos AProtopapadakis EDoulamis NMakantasis K(2020)Automatic 3D Modeling and Reconstruction of Cultural Heritage Sites from Twitter ImagesSustainability10.3390/su1210422312:10(4223)Online publication date: 21-May-2020
https://doi.org/10.3390/su12104223
Rallis IMarkoulidakis IGeorgoulas IKopsiaftis GMakedon F(2020)A novel classification method for customer experience survey analysisProceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3389189.3397999(1-9)Online publication date: 30-Jun-2020
https://dl.acm.org/doi/10.1145/3389189.3397999
Protopapadakis EVoulodimos ADoulamis N(2017)An investigation on multi-objective optimization of feedforward neural network topology2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA.2017.8316455(1-6)Online publication date: Aug-2017
https://doi.org/10.1109/IISA.2017.8316455
Protopapadakis EVoulodimos ADoulamis A(2017)Data sampling for semi-supervised learning in vision-based concrete defect recognition2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA.2017.8316454(1-6)Online publication date: Aug-2017
https://doi.org/10.1109/IISA.2017.8316454
Chen JZhou XJin Q(2016)Gradually adaptive recommendation based on semantic mapping of users' interest correlationsInternational Journal of Communication Systems10.1002/dac.283529:2(341-361)Online publication date: 25-Jan-2016
https://dl.acm.org/doi/10.1002/dac.2835

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