Identifying users profiles from mobile calls habits

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 17-24

  Furletti, Barbara ^a   Gabrielli, Lorenzo ^a   Renso, Chiara ^a   Rinzivillo, Salvatore ^a  

^a ISTI CNR   (Italy)

Author keywords

GSM data; SOM; user profiles

Indexed keywords

BOTTOM UP; DATA SETS; GSM PHONES; MOBILE CALLS; PHONE CALLS; POSITIONING DATA; RESEARCH QUESTIONS; SOM; TOPDOWN; TRAFFIC EFFECT; USER PROFILE;

TELEPHONE;

GLOBAL SYSTEM FOR MOBILE COMMUNICATIONS;

EID: 84866036736     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2346496.2346500     Document Type: Conference Paper

References (15)

1. R. Ahas, S. Silm, S. Järv, and E. Saluveer. Using mobile positioning data to model locations meaningful to users of mobile phones. Journal of Urban Technology, 17(1), 2010.
2. G. Andrienko, N. Andrienko, P. Bak, S. Bremm, D. Keim, T. von Landesberger, C Poelitz, and T. Schreck. A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage. Journal of Location Based Services, 4(3-4), 2010.
3. F. Calabrese, M. Colonna, P. Lovisolo, D. Parata, and C. Ratti. Real-time urban monitoring using cell phones: A case study in rome. IEEE Transactions on Intelligent Transportation Systems, 12:141-151, 2011.
4. F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, C. Renso, S. Rinzivillo, and R. Trasarti. Unveiling the complexity of human mobility by querying and mining massive trajectory data. VLDB J., 20(5):695-719, 2011.
5. T. Kohonen. Self-Organizing Maps. Springer Series in Information Sciences. Springer, 2001.
6. L. Liao, D. Fox, and H. Kautz. Location-based activity recognition using relational markov networks. In Proceedings of the Nineteenth International Conference on Artificial Intelligence, IJCAI'05, 2005.
7. Nokia. Nokia siemens networks. http://www.slideshare.net/ NokiaSiemensNetworks/20-years-of-gsm-past-present-future-8512655.
8. C. Parent, S. Spaccapietra, C. Renso, G. Andrienko, N. Andrienko, V. Bogorny, M. L. Damiani, A. Gkoulalas-Divanis, J. A. Macedo, N. Pelekis, Y. Theodoridis, and Z. Yan. Semantic trajectories modeling and analysis. Accepted at ACM Computing Surveys, 2012.
9. F. C. Pereira, L. Liu, and F. Calabrese. Profiling transport demand for planned special events: Prediction of public home distributions, 2010. Available online at www.scienceDirect.com.
10. D. Quercia, N. Lathia, F. Calabrese, G. Di Lorenzo, and J. Crowcroft. Recommending social events from mobile phone location data. In International Conference on Data Mining, ICDM, pages 971-976, 2010.
11. C. Ratti, A. Sevtsuk, S. Huang, and R. Pailer. Mobile landscapes: Graz in real time, 2005. MIT Senseable City Lab.
12. J. Schlaich, T. Otterstätter, and M. Friedrich. Generating trajectories from mobile phone data. In Proceedings of the 89th Annual Meeting Compendium of Papers, Transportation Research Board of the National Academies, 2010.
13. Wikipedia. Call data record. http://en.wikipedia.org/wiki/Call-detail- record.
14. Wikipedia. Tourism. http://en.wikipedia.org/wiki/Tourism.
15. Xiangye Xiao, Yu Zheng, Qiong Luo, and Xing Xie. Finding similar users using category-based location history. In GIS, pages 442-445, 2010.