ABSTRACT
The huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- T. Kohonen. Self-Organizing Maps. Springer Series in Information Sciences. Springer, 2001. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Nokia. Nokia siemens networks. http://www.slideshare.net/NokiaSiemensNetworks/20-years-of-gsm-past-present-future-8512655.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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. Google ScholarDigital Library
- C. Ratti, A. Sevtsuk, S. Huang, and R. Pailer. Mobile landscapes: Graz in real time, 2005. MIT Senseable City Lab.Google Scholar
- 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.Google Scholar
- Wikipedia. Call data record. http://en.wikipedia.org/wiki/Call_detail_record.Google Scholar
- Wikipedia. Tourism. http://en.wikipedia.org/wiki/Tourism.Google Scholar
- Xiangye Xiao, Yu Zheng, Qiong Luo, and Xing Xie. Finding similar users using category-based location history. In GIS, pages 442--445, 2010. Google ScholarDigital Library
Index Terms
- Identifying users profiles from mobile calls habits
Recommendations
Profiling the Mobile Phone Users and Their Relationship to the Internet Services and Portals
ICMB '11: Proceedings of the 2011 10th International Conference on Mobile BusinessThe purpose of this study is to identify the relationship between mobile consumers' attitude and their demographic characteristics. New variables such as Personal Technological Innovativeness and Mobile Phone Design are constructed and their association ...
Textual information retrieval with user profiles using fuzzy clustering and inferencing
Intelligent exploration of the webWe present a fuzzy-logic based approach to construction and use of user profiles in web textual information retrieval. A classical user profile is a collection of terms extracted from the set of documents for a specific user or a group of users. We use ...
A User Profiles Acquiring Approach Using Pseudo-Relevance Feedback
RSKT '09: Proceedings of the 4th International Conference on Rough Sets and Knowledge TechnologyUser profiles are important in personalized Web information gathering and recommendation systems. The current user profiles acquiring techniques however suffer from some problems and thus demand to improve. In this paper, a survey of the existing user ...
Comments