Toward personalized context recognition for mobile users: A semisupervised bayesian HMM approach

ACM Transactions on Knowledge Discovery from Data

Volumn 9, Issue 2, 2014, Pages

  Huai, Baoxing ^a   Chen, Enhong ^a   Zhu, Hengshu ^a   Xiong, Hui ^b   Bao, Tengfei ^a   Liu, Q I ^a   Tian, Jilei ^c  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b RUTGERS UNIVERSITY   (United States)

^c NOKIA RESEARCH CENTER   (United States)

Author keywords

Context recognition; Hidden markov model

Indexed keywords

HIDDEN MARKOV MODELS; INFORMATION SERVICES; LEARNING ALGORITHMS; MIXTURES; PROBABILITY DISTRIBUTIONS; SEMANTICS;

CONTEXT AWARE SERVICES; CONTEXT RECOGNITION; CONTEXTUAL FEATURE; CONTEXTUAL INFORMATION; DIRICHLET PROCESS MIXTURES (DPM); LABELED AND UNLABELED DATA; MOBILE ENVIRONMENTS; PROBABILISTIC DISTRIBUTION;

BEHAVIORAL RESEARCH;

EID: 84907555913     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/2629504     Document Type: Article

References (50)

1. G. D. Abowd, C. G. Atkeson, J. Hong, S. Long, R. Kooper, and M. Pinkerton. 1997. Cyberguide: A mobile context-aware tour guide. Wireless Networks 3, 5, 421-433.
2. J. H. Albert and S. Chib. 1993. Bayes inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts. Journal of Business & Economic Statistics 11, 1, 1-15.
3. C. B. Anagnostopoulos, A. Tsounis, and S. Hadjiefthymiades. 2007. Context awareness in mobile computing environments. Wireless Personal Communications 42, 3, 445-464.
4. C. E. Antoniak. 1974. Mixtures of dirichlet processes with applications to Bayesian nonparametric problems. Annals of Statistics 1152-1174.
5. L. Azzopardi, M. Girolami, and K. Van Risjbergen. 2003. Investigating the relationship between language model perplexity and IR precision-recall measures. In Proceedings of the 26th Annual International ACMSIGIR Conference on Research and Development in Information Retrieval. ACM, 369-370.
6. T. Bao, H. Cao, E. Chen, J. Tian, and H. Xiong. 2012. An unsupervised approach to modeling personalized contexts of mobile users. Knowledge and Information Systems 31, 2, 345-370.
7. L. E. Baum, T. Petrie, G. Soules, and N. Weiss. 1970. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics 41, 1, 164-171.
8. P. Belimpasakis, K. Roimela, and Y. You. 2009. Experience explorer: A life-logging platform based on mobile context collection. In Proceedings of the 3rd International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST'09). IEEE, 77-82.
9. D. M. Blei and J. D. Lafferty. 2006. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning. ACM, 113 -120.
10. D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993 -1022.
11. H. Cao, T. Bao, Q. Yang, E. Chen, and J. Tian. 2010. An effective approach for mining mobile user habits. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, 1677-1680.
12. H. Cao, D. Jiang, J. Pei, E. Chen, and H. Li. 2009. Towards context-aware search by learning a very large variable length hidden Markov model from search logs. In Proceedings of the 18th International Conference on World Wide Web. ACM, 191-200.
13. S. Chu. 2008. Unstructured audio classification for environment recognition. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence. 13-17.
14. G. A. Churchill. 1989. Stochastic models for heterogeneous dna sequences. Bulletin of Mathematical Biology 51, 1, 79-94.
15. N. Eagle and A. S. Pentland. 2009. Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology 63, 7, 1057-1066.
16. A. J. Kronen, V. T. Peltonen, J. T. Tuomi, A. P. Klapuri, S. Fagerlund, T. Sorsa, G. Lorho, and J. Huopaniemi. 2006. Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing, 14, 1, 321-329.
17. D. R. Fredkin and J. A. Rice. 1992. Bayesian restoration of single-channel patch clamp recordings. Biometrics 427-448.
18. S. Goldwater and T. Griffiths. 2007. A fully Bayesian approach to unsupervised part-of-speech tagging. In Annual Meeting: Association for Computational Linguistics. Vol. 45. 744.
19. S. Guha, L. Yi, and D. Neuberg. 2008. Bayesian hidden Markov modeling of array CGH data. Journal of the American Statistical Association 103, 485-497.
20. J. D. Hamilton. 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the Econometric Society, 357-384.
21. G. Heinrich. 2005. Parameter Estimation for Text Analysis. Retrieved from http://www.arbylon.net/ publications/text-est.pdf.
22. J. Himberg, K. Korpiaho, H. Mannila, J. Tikanmaki, and H. T. Toivonen. 2001. Time series segmentation for context recognition in mobile devices. In Proceedings of the IEEE International Conference on Data Mining (ICDM'01). IEEE, 203-210.
23. R. Huang, G. Yu, and Z. Wang. 2013. Dirichlet process mixture model for document clustering with feature partition. IEEE Transactions on Knowledge and Data Engineering 25, 8, 1748-1759.
24. H. Ishwaran and L. F. James. 2001. Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association 96, 453, 161-173.
25. B. -H. Juang and L. R. Rabiner. 1991. Hidden Markov models for speech recognition. Technometrics 33, 3, 251-272.
26. P. Korpipää, M. Koskinen, J. Peltola, S.-M. Mäkelä, and T. Seppänen. 2003. Bayesian approach to sensor-based context awareness. Personal and Ubiquitous Computing 7, 2, 113-124.
27. C. Leggetter and P. Woodland. 1995. Maximum likelihood linear regression for speaker adaptation of con- tinuous density hidden Markov models. Computer Speech and Language 9, 2, 171.
28. T. Lemlouma and N. Layaïda. 2004. Context-aware adaptation for mobile devices. In Proceedings of the 2004 IEEE International Conference on Mobile Data Management. IEEE, 106-111.
29. B. G. Leroux and M. L. Puterman. 1992. Maximum-penalized-likelihood estimation for independent and Markov-dependent mixture models. Biometrics 545-558.
30. X. Li, H. Cao, E. Chen, and J. Tian. 2012. Learning to infer the status of heavy-duty sensors for energy-efficient context-sensing. ACM Transactions on Intelligent Systems and Technology (TIST) 3, 2, 35.
31. N. Liampotis, N. Kalatzis, I. Roussaki, P. Kosmides, I. Papaioannou, E. Sykas, M. Anagnostou, and S. Xynogalas. 2012. Addressing the context-awareness requirements in personal smart spaces. In Proceedings of the 2012 IEEE Asia-Pacific Services Computing Conference (APSCCT2). IEEE, 281 -285.
32. L. Liao, D. J. Patterson, D. Fox, and H. Kautz. 2007. Building personal maps from gps data. Annals of the New York Academy of Sciences 1093, 1, 249-265.
33. J. S. Liu, A. F. Neuwald, and C. E. Lawrence. 1999. Markovian structures in biological sequence alignments. Journal of the American Statistical Association 94, 445, 1-15.
34. H. Ma, H. Cao, Q. Yang, E. Chen, and J. Tian. 2012. A habit mining approach for discovering similar mobile users. In Proceedings of the 21st International Conference on World Wide Web. ACM, 231-240.
35. R. M. Neal. 2000. Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics 9, 2, 249-265.
36. B. Nham, K. Siangliulue, and S. Yeung. 2008. Predicting mode of transport from iPhone accelerometer data. Technical report, Stanford University.
37. K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell. 2000. Text classification from labeled and unlabeled documents using em. Machine Learning 39, 2, 103-134.
38. B. Peng, Y. Wang, and J.-T. Sun. 2012. Mining mobile users activities based on search query text and context. In Advances in Knowledge Discovery and Data Mining. Springer, 109-120.
39. A. Pievatolo, F. Ruggeri, and R. Soyer. 2012. A Bayesian hidden Markov model for imperfect debugging. Reliability Engineering & Systems Safety 103, 11-21.
40. L. R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 2, 257-286.
41. N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman. 2005. Activity recognition from accelerometer data. In Proceedings of the National Conference on Artificial Intelligence. Vol. 20. AAAI Press, 1541.
42. R. Rawassizadeh, M. Tomitsch, K. Wac, and A. M. Tjoa. 2012. Ubiqlog: A generic mobile phone-based life-log framework. Personal and Ubiquitous Computing, 1-17.
43. P. Re snik and E. Hardisty. 2010. Gibbs sampling for the uninitiated. Technical report, DTIC Document.
44. B. Schilit, N. Adams, and R. Want. 1994. Context-aware computing applications. In Proceedings of the 1st Workshop on Mobile Computing Systems and Applications (WMCSA94). IEEE, 85-90.
45. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. 2006. Hierarchical Dirichlet processes. Journal of the American Statistical Association 101, 476, 1566-1581.
46. T. Teraoka. 2011. A study of exploration of heterogeneous personal data collected from mobile devices and web services. In Proceedings of the 5th FTRA International Conference on Multimedia and Ubiquitous Engineering (MUE'11. IEEE, 239-245.
47. K. Yu, B. Zhang, H. Zhu, H. Cao, and J. Tian. 2012. Towards personalized context-aware recommendation by mining context logs through topic models. In Proceedings of the 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Springer-Verlag, Kuala Lumpur, Malaysia, 431-443.
48. Y. Zheng, L. Liu, L. Wang, and X. Xie. 2008. Learning transportation mode from raw GPS data for geographic applications on the web. In Proceedings of the 17th International Conference on World Wide Web. ACM, 247-256.
49. H. Zhu, H. Cao, E. Chen, H. Xiong, and J. Tian. 2012a. Exploiting enriched contextual information for mobile app classification. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM, 1617-1621.
50. H. Zhu , E.Chen, K.Yu, H.Cao, H.Xiong, and J.Tian. 2012. Mining personal context-aware preferences for mobile users. In Proceedings of the IEEE 12th International Conference on Data Mining (ICDM'12). IEEE, 1212-1217.