article

An unsupervised approach to modeling personalized contexts of mobile users

Authors:

Hui XiongAuthors Info & Claims

Knowledge and Information Systems, Volume 31, Issue 2

Pages 345 - 370

Published: 01 May 2012 Publication History

Abstract

Mobile context modeling is a process of recognizing and reasoning about contexts and situations in a mobile environment, which is critical for the success of context-aware mobile services. While there are prior works on mobile context modeling, the use of unsupervised learning techniques for mobile context modeling is still under-explored. Indeed, unsupervised techniques have the ability to learn personalized contexts, which are difficult to be predefined. To that end, in this paper, we propose an unsupervised approach to modeling personalized contexts of mobile users. Along this line, we first segment the raw context data sequences of mobile users into context sessions where a context session contains a group of adjacent context records which are mutually similar and usually reflect the similar contexts. Then, we exploit two methods for mining personalized contexts from context sessions. The first method is to cluster context sessions and then to extract the frequent contextual feature-value pairs from context session clusters as contexts. The second method leverages topic models to learn personalized contexts in the form of probabilistic distributions of raw context data from the context sessions. Finally, experimental results on real-world data show that the proposed approach is efficient and effective for mining personalized contexts of mobile users.

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Bessghaier NSoui MKolski CChouchane M(2021)On the Detection of Structural Aesthetic Defects of Android Mobile User Interfaces with a Metrics-based ToolACM Transactions on Interactive Intelligent Systems10.1145/341046811:1(1-27)Online publication date: 15-Mar-2021
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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 31, Issue 2

May 2012

206 pages

ISSN:0219-1377

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Copyright © Copyright © 2012 Springer-Verlag London Limited.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 May 2012

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Cited By

Bessghaier NSoui MKolski CChouchane M(2021)On the Detection of Structural Aesthetic Defects of Android Mobile User Interfaces with a Metrics-based ToolACM Transactions on Interactive Intelligent Systems10.1145/341046811:1(1-27)Online publication date: 15-Mar-2021
https://dl.acm.org/doi/10.1145/3410468
Xi DZhuang FLiu YGu JXiong HHe Q(2019)Modelling of bi-directional spatio-temporal dependence and users' dynamic preferences for missing POI check-in identificationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33015458(5458-5465)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33015458
Liu HLi THu RFu YGu JXiong H(2019)Joint representation learning for multi-modal transportation recommendationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33011036(1036-1043)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33011036
Soui MChouchane MMkaouer MKessentini MGhedira K(2019)Assessing the quality of mobile graphical user interfaces using multi-objective optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-019-04391-824:10(7685-7714)Online publication date: 8-Oct-2019
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Xiao ZChen JZhu JMcIlraith SWeinberger K(2018)Towards training probabilistic topic models on neuromorphic multi-chip systemsProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504826(6459-6466)Online publication date: 2-Feb-2018
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Liu YLiu CLu XTeng MZhu HXiong HMatwin SYu SFarooq F(2017)Point-of-Interest Demand Modeling with Human Mobility PatternsProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3098168(947-955)Online publication date: 13-Aug-2017
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