Abstract
Nowadays, the accumulation of people's whereabouts due to location-based applications has made it possible to construct their mobility profiles. This access to users' mobility profiles subsequently brings benefits back to location-based applications. For instance, in on-line social networks, friends can be recommended not only based on the similarity between their registered information, for instance, hobbies and professions but also referring to the similarity between their mobility profiles.
In this article, we propose a new approach to construct and compare users' mobility profiles. First, we improve and apply frequent sequential pattern mining technologies to extract the sequences of places that a user frequently visits and use them to model his mobility profile. Second, we present a new method to calculate the similarity between two users using their mobility profiles. More specifically, we identify the weaknesses of a similarity metric in the literature, and propose a new one which not only fixes the weaknesses but also provides more precise and effective similarity estimation. Third, we consider the semantics of spatio-temporal information contained in user mobility profiles and add them into the calculation of user similarity. It enables us to measure users' similarity from different perspectives. Two specific types of semantics are explored in this article: location semantics and temporal semantics. Last, we validate our approach by applying it to two real-life datasets collected by Microsoft Research Asia and Yonsei University, respectively. The results show that our approach outperforms the existing works from several aspects.
- R. Agrawal and R. Srikant. 1995. Mining sequential patterns. In Proceedings of the 11th International Conference on Data Engineering. IEEE, 3--14. Google ScholarDigital Library
- M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander. 1999. OPTICS: Ordering points to identify the clustering structure. In Proceedings of the 20th ACM SIGMOD International Conference on Management of Data. ACM Press, 49--60. Google ScholarDigital Library
- M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. 2000. LOF: Identifying density-based local outliers. In Proceedings of the 21st ACM SIGMOD International Conference on Management of Data. ACM Press, 93--104. Google ScholarDigital Library
- T. Brinkhoff. 2002. A framework for generating network-based moving objects. GeoInformatica 6, 2, 153--180. Google ScholarDigital Library
- X. Chen, J. Pang, and R. Xue. 2013. Constructing and comparing user mobility profiles for location-based services. In Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM Press, 261--266. Google ScholarDigital Library
- Y. Chon, E. Talipov, H. Shin, and H. Cha. 2011. Mobility prediction-based smartphone energy optimization for everyday location monitoring. In Proceedings of the 9th International Conference on Embedded Networked Sensor Systems. ACM Press, 82--95. Google ScholarDigital Library
- F. Giannotti, M. Nanni, D. Pedreschi, and F. Pinelli. 2006. Mining sequences with temporal annotations. In Proceedings of the 21st ACM Symposium on Applied Computing. ACM Press, 593--597. Google ScholarDigital Library
- F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, and M. Axiak. 2007. Trajectory pattern mining. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 330--339. Google ScholarDigital Library
- A. K. Jain and R. C. Dubes. 1988. Algorithms for Clustering Data. Prentice-Hall, Inc. Google ScholarDigital Library
- D. Krajzewicz, J. Erdmann, M. Behrisch, and L. Bieker. 2012. Recent Development and Applications of SUMO: Simulation of Urban MObility. Int. J. Adv. Syst. Measure. 5, 3&4, 128--138. http://elib.dlr.de/80483/.Google Scholar
- Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and W.-Y. Ma. 2008. Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems. ACM Press, 34--43. Google ScholarDigital Library
- 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 World Wide Web Conference. ACM Press, 231--240. Google ScholarDigital Library
- A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti. 2009. WhereNext: A location predictor on trajectory pattern mining. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 637--646. Google ScholarDigital Library
- J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. 2004. Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE Trans. Knowl. Data Eng. 16, 11, 1424--1440. Google ScholarDigital Library
- I. Rhee, M. Shin, S. Hong, K. Lee, S. J. Kim, and S. Chong. 2011. On the levy-walk nature of human mobility. IEEE/ACM Trans. Networking 19, 3, 630--643. Google ScholarDigital Library
- C. Song, T. Koren, P. Wang, and A.-L. Barabási. 2010. Modelling the scaling properties of human mobility. Nature Physics 6, 818--823.Google ScholarCross Ref
- M. Reaz Uddin, C. V. Ravishankar, and V. J. Tsotras. 2011. Finding regions of interest from trajectory data. In Proceedings of the 12th IEEE International Conference on Mobile Data Management. IEEE, 39--48. Google ScholarDigital Library
- X. Xiao, Y. Zheng, Q. Luo, and X. Xie. 2010. Finding similar users using category-based location history. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM Press, 442--445. Google ScholarDigital Library
- Z. Yan, D. Chakraborty, C. Parent, S. Spaccapietra, and K. Aberer. 2011. SeMiTri: A framework for semantic annotation of heterogeneous trajectories. In Proceedings of the 14th International Conference on Extending Database Technology. ACM Press, 259--270. Google ScholarDigital Library
- M. Ye, D. Shou, W.-C. Lee, P. Yin, and K. Janowicz. 2011. On the semantic annotation of places in location-based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 520--528. Google ScholarDigital Library
- J.-C. Ying, H.-C. Lu, W.-C. Lee, T.-C.Weng, and S. Tseng. 2010. Mining user similarity from semantic trajectories. In Proceedings of the 2nd ACM SIGSPATIAL Workshop on Location Based Social Networks. ACM Press, 19--26. Google ScholarDigital Library
- Y. Zheng. 2012. Personal communication.Google Scholar
- Y. Zheng, L.Wang, R. Zhang, X. Xie, and W.-Y. Ma. 2008. GeoLife: Managing and understanding your past Life over maps. In Proceedings of the 9th International Conference on Mobile Data Management. IEEE, 211--212. Google ScholarDigital Library
- Y. Zheng, L. Zhang, Z. Ma, X. Xie, and W.-Y. Ma. 2011. Recommending friends and locations based on individual location history. ACM Trans. Web 5, 1, 1--44. Google ScholarDigital Library
- Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th International Conference on World Wide Web. ACM Press, 791--800. Google ScholarDigital Library
Index Terms
- Constructing and Comparing User Mobility Profiles
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