Toward Personalized Context Recognition for Mobile Users

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 ...

[1]  Henry Kautz,et al.  Building Personal Maps from GPS Data , 2006, Annals of the New York Academy of Sciences.

[2]  Jian-Tao Sun,et al.  Mining Mobile Users' Activities Based on Search Query Text and Context , 2012, PAKDD.

[3]  G. Churchill.  Stochastic models for heterogeneous DNA sequences. , 1989, Bulletin of mathematical biology.

[4]  Enhong Chen,et al.  An effective approach for mining mobile user habits , 2010, CIKM.

[5]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[6]  Gregory D. Abowd,et al.  Cyberguide: A mobile context‐aware tour guide , 1997, Wirel. Networks.

[7]  Yi Li,et al.  Bayesian Hidden Markov Modeling of Array CGH Data , 2008, Journal of the American Statistical Association.

[8]  Jun S. Liu,et al.  Markovian structures in biological sequence alignments , 1999 .

[9]  Tapio Seppänen,et al.  Bayesian approach to sensor-based context awareness , 2003, Personal and Ubiquitous Computing.

[10]  Kimmo Roimela,et al.  Experience Explorer: A Life-Logging Platform Based on Mobile Context Collection , 2009, 2009 Third International Conference on Next Generation Mobile Applications, Services and Technologies.

[11]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[12]  Tayeb Lemlouma,et al.  Context-aware adaptation for mobile devices , 2004, IEEE International Conference on Mobile Data Management, 2004. Proceedings. 2004.

[13]  Heikki Mannila,et al.  Time series segmentation for context recognition in mobile devices , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[14]  Stathes Hadjiefthymiades,et al.  Context Awareness in Mobile Computing Environments , 2007, Wirel. Pers. Commun..

[15]  Lancelot F. James,et al.  Gibbs Sampling Methods for Stick-Breaking Priors , 2001 .

[16]  James D. Hamilton.  A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle , 1989 .

[17]  Hui Xiong,et al.  An Unsupervised Approach to Modeling Personalized Contexts of Mobile Users , 2010, ICDM.

[18]  Bill N. Schilit,et al.  Context-aware computing applications , 1994, Workshop on Mobile Computing Systems and Applications.

[19]  Vesa T. Peltonen,et al.  Audio-based context recognition , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[20]  Jun Zhang,et al.  Dirichlet Process Mixture Model for Document Clustering with Feature Partition , 2013, IEEE Transactions on Knowledge and Data Engineering.

[21]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[22]  S. Chib,et al.  Bayes inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts , 1993 .

[23]  Hui Xiong,et al.  Mining Personal Context-Aware Preferences for Mobile Users , 2012, 2012 IEEE 12th International Conference on Data Mining.

[24]  Biing-Hwang Juang,et al.  Hidden Markov Models for Speech Recognition , 1991 .

[25]  Teruhiko Teraoka,et al.  A Study of Exploration of Heterogeneous Personal Data Collected from Mobile Devices and Web Services , 2011, 2011 Fifth FTRA International Conference on Multimedia and Ubiquitous Engineering.

[26]  Enhong Chen,et al.  A habit mining approach for discovering similar mobile users , 2012, WWW.

[27]  Philip Resnik,et al.  GIBBS SAMPLING FOR THE UNINITIATED , 2010 .

[28]  Katarzyna Wac,et al.  UbiqLog: a generic mobile phone-based life-log framework , 2013, Personal and Ubiquitous Computing.

[29]  Hui Xiong,et al.  Exploiting enriched contextual information for mobile app classification , 2012, CIKM '12.

[30]  C. Antoniak.  Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .

[31]  C. J. van Rijsbergen,et al.  Investigating the relationship between language model perplexity and IR precision-recall measures , 2003, SIGIR.

[32]  Serena Yeung,et al.  Predicting Mode of Transport from iPhone Accelerometer Data , 2012 .

[33]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[34]  Miltiades E. Anagnostou,et al.  Addressing the Context-Awareness Requirements in Personal Smart Spaces , 2012, 2012 IEEE Asia-Pacific Services Computing Conference.

[35]  Michael I. Jordan,et al.  Hierarchical Dirichlet Processes , 2006 .

[36]  M. Puterman,et al.  Maximum-penalized-likelihood estimation for independent and Markov-dependent mixture models. , 1992, Biometrics.

[37]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[38]  Philip C. Woodland,et al.  Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models , 1995, Comput. Speech Lang..

[39]  Thomas L. Griffiths,et al.  A fully Bayesian approach to unsupervised part-of-speech tagging , 2007, ACL.