Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Toward Personalized Context Recognition for Mobile Users: A Semisupervised Bayesian HMM Approach

Toward Personalized Context Recognition for Mobile Users: A Semisupervised Bayesian HMM Approach Toward Personalized Context Recognition for Mobile Users: A Semisupervised Bayesian HMM Approach BAOXING HUAI, ENHONG CHEN, and HENGSHU ZHU, University of Science and Technology of China HUI XIONG, Rutgers University TENGFEI BAO and QI LIU, University of Science and Technology of China JILEI TIAN, Nokia The problem of mobile context recognition targets the identification of semantic meaning of context in a mobile environment. This plays an important role in understanding mobile user behaviors and thus provides the opportunity for the development of better intelligent context-aware services. A key step of context recognition is to model the personalized contextual information of mobile users. Although many studies have been devoted to mobile context modeling, limited efforts have been made on the exploitation of the sequential and dependency characteristics of mobile contextual information. Also, the latent semantics behind mobile context are often ambiguous and poorly understood. Indeed, a promising direction is to incorporate some domain knowledge of common contexts, such as "waiting for a bus" or "having dinner," by modeling both labeled and unlabeled context data from mobile users because there are often few labeled contexts available in practice. To this end, in this article, we propose a sequence-based semisupervised approach http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Toward Personalized Context Recognition for Mobile Users: A Semisupervised Bayesian HMM Approach

Download PDF
29 pages

Loading next page...
 
/lp/association-for-computing-machinery/toward-personalized-context-recognition-for-mobile-users-a-XlJc112nlk

References (53)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/2629504
Publisher site
See Article on Publisher Site

Abstract

Toward Personalized Context Recognition for Mobile Users: A Semisupervised Bayesian HMM Approach BAOXING HUAI, ENHONG CHEN, and HENGSHU ZHU, University of Science and Technology of China HUI XIONG, Rutgers University TENGFEI BAO and QI LIU, University of Science and Technology of China JILEI TIAN, Nokia The problem of mobile context recognition targets the identification of semantic meaning of context in a mobile environment. This plays an important role in understanding mobile user behaviors and thus provides the opportunity for the development of better intelligent context-aware services. A key step of context recognition is to model the personalized contextual information of mobile users. Although many studies have been devoted to mobile context modeling, limited efforts have been made on the exploitation of the sequential and dependency characteristics of mobile contextual information. Also, the latent semantics behind mobile context are often ambiguous and poorly understood. Indeed, a promising direction is to incorporate some domain knowledge of common contexts, such as "waiting for a bus" or "having dinner," by modeling both labeled and unlabeled context data from mobile users because there are often few labeled contexts available in practice. To this end, in this article, we propose a sequence-based semisupervised approach

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery

Published: Sep 23, 2014

There are no references for this article.