Abstract
The location-based social networks have been becoming flourishing in recent years. In this paper, we aim to estimate the similarity between users according to their physical location histories (represented by GPS trajectories). This similarity can be regarded as a potential social tie between users, thereby enabling friend and location recommendations. Different from previous work using social structures or directly matching users’ physical locations, this approach model a user’s GPS trajectories with a semantic location history (SLH), e.g., shopping malls → restaurants → cinemas. Then, we measure the similarity between different users’ SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user’s interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. When matching SLHs, we consider the sequential property, the granularity and the popularity of semantic locations. We evaluate our method based on a real-world GPS dataset collected by 109 users in a period of 1 year. The results show that SLH outperforms a physical-location-based approach and MTM is more effective than several widely used sequence matching approaches given this application scenario.
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References
Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquitous Comput 7(5):275–286
Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering, In: Proceedings of the international 14th conference on uncertainty in artificial intelligence, Madison, Wisconsin, USA, July 1998
Cao X, Cong G, Jensen CS (2010) Mining significant semantic locations from GPS Data. In: Proceedings of the VLDB Endowment
Chen Z, Shen H T, Zhou X, Zheng Y, Xie X (2010) Searching trajectories by locations—an efficiency study. In: Proceedings of ACM SIGMOD International Conference on Management of Data. ACM Press, New York, pp 255–266
Cranshaw J, Toch E, Hong J, Kittur A, Sadeh N (2010) Bridging the gap between the physical location and online social networks. In: Proceedings of the International Conference on Ubiquitous Computing. ACM Press, New York
Eagle N, Pentland A (2006) Reality mining: sensing complex social systems. Pers Ubiquitous Comput 10(4):255–268
Eagle N, Pentland A, Lazer D (2009a) Inferring social network structure using mobile phone data. Proc Nat Acad Sci (PNAS) 106(36):15274–15278
Eagle N, Montjoye Y-A, Bettencourt LMA (2009b) Community computing: comparisons between rural and urban societies using mobile phone data. IEEE Soc Comput 144–150
Froehlich J, Chen M, Smith I, Potter F (2006) Voting with your feet: an investigative study of the relationship between place visit behavior and preference. In: Proceedings of the international conference on ubiquitous computing. ACM Press, New York
Giannotti F, Nanni M, Pedreschi D, Pinelli F (2007) Trajectory pattern mining. In: Proceedings of the 13rd ACM SIGKDD conference on knowledge discovery and data mining, San Jose, CA, USA, August 2007. ACM Press, New York, pp 330–339
Hariharn R, Toyama K (2004) Project Lachesis: parsing and modeling location histories. In: Proceedings of the 3rd international conference on geographic information science, Park, Utah, October 2004, pp 106–124
Hung CC, Chang CW, Peng WC (2009) Mining trajectory profiles for discovering user communities. In: Proceedings of ACM SIGSPATIAL GIS workshop on location based social networks
Jarvelin K, Kekalainen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst 22(1):422–446
Krumm J, Horvitz E (2007) Predestination: where do you want to go today? IEEE Comput Mag 40(4):105–107
Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Sov Phys Dokl 10:707–710
Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma WY (2008) Mining user similarity based on location history. In: Proceeding of the 16th international conference on advances in geographic information system. ACM Press, New York, pp 1–10
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80
Liu G, Wolfson O, Yin H (2006) Extracting semantic location from outdoor positioning systems. In: Proceedings of the 7th international conference on mobile data management. IEEE, p 73
Patterson D, Liao L, Fox D, Kautz H (2003) Inferring high-level behavior from low-level sensors. In: Proceedings of the 8th international conference on ubiquitous computing. Springer, Berlin, pp 73–89
Sarwar B, Karypis G, Konstan J, Riedl J (2000) Application of dimensionality reduction recommender system—a case study. In: Proceeding of ACM WebKDD Workshop, Boston, MA
Takeuchi Y, Sugimoto M (2006) CityVoyager: an outdoor recommendation system based on user location history. In: Proceedings of the 3rd International Conference Ubiquitous Intelligence and Computing, Wuhan, China, September 2006. Springer press, Berlin, pp 625–636
Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings of the international conference on data engineering
Xiao X, Zheng Y, Luo Q, Xie X (2010) Finding similar users using category-based location history. Poster. In: Proceedings of ACM SIGSPATIAL conference on advances in geographical information systems
Ye Y, Zheng Y, Chen Y, Feng J, Xie X (2009) Mining individual life pattern based on location history. In: Proceedings of the international conference on mobile data management. IEEE press, pp 1–10
Yi B, Jagdish H, Faloutsos C (1998) Efficient retrieval of similar time sequences under time warping. In: Proceedings of the international conference on data engineering
Zhang D, Guo B, Yu Z (2011) The emergence of social and community intelligence. Computer 44(7):21–28
Zheng Y (2011) Location-based social networks: users. In: Zheng Y, Zhou X (eds) Computing with spatial trajectories, 1st edn. Springer, New York
Zheng Y, Xie X (2009b) Learning location correlation from GPS trajectories. In: Proceedings of the international conference on mobile data management, IEEE Press, pp 27–32
Zheng Y, Xie X (2011a) Location-based social networks: locations. In: Zheng Y, Zhou X (eds) Computing with spatial trajectories, 1st edn. Springer, New York
Zheng Y, Xie X (2011b) Learning travel recommendations from user-generated GPS traces. ACM Trans Intell Syst Technol 2(1):2–29
Zheng Y, Zhou X (2011) Computing with spatial trajectories. Springer, New York. ISBN 978-1-4614-1628-9
Zheng Y, Li Q, Chen Y, Xie X, Ma WY (2008a) Understanding mobility based on GPS data. In: Proceedings of 10th International Conference on Ubiquitous Computing, Seoul, South Korea, September 2008. ACM Press, pp 312–321
Zheng Y, Liu L, Wang L, Xie X (2008b) Learning transportation mode from raw GPS data for geographic applications on the Web. In: Proceedings of the 11th international conference on world wide web. ACM Press, New York, pp 247–256
Zheng Y, Wang L, Zhang R, Xie X, Ma WY (2008c) GeoLife: managing and understanding your past life over maps. In: Proceedings of the 9th international conference on mobile data management. IEEE Press, pp 211–212
Zheng Y, Chen Y, Xie X, Ma WY (2009a) GeoLife2.0: a location-based social networking service. In: Proceedings of International Conference on Mobile Data Management 2009. IEEE Press, pp 357–358
Zheng Y, Zhang L, Xie X, Ma WY (2009c) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of 18th international conference on world wild web, ACM Press, New York pp 791–800
Zheng VW, Cao B, Zheng Y, Xie X, Yang Q (2010a) Collaborative filtering meets mobile recommendation: a user-centered approach. In: Proceedings of 24th AAAI conference on Artificial Intelligence, Atlanta, USA, July 2010. AAAI press, pp 236–241
Zheng Y, Chen Y, Li Q, Xie X, Ma WY (2010b) Understanding transportation modes based on GPS data for web applications. ACM Trans Web 4(1):1–36
Zheng Y, Xie X, Ma WY (2010c) GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Date Eng Bull 33(2):32–40
Zheng VW, Zheng Y, Xie X, Yang Q (2010d) Collaborative location and activity recommendations with GPS History Data. In: Proceeding of the 19th international conference on world wide web, ACM Press, New York, pp 1029–1038
Zheng Y, Zhang L, Ma Z, Xie X, Ma WY (2011) Recommending friends and locations based on individual location history. ACM Trans Web 5(1):5–44
GeoLife GPS trajectories (2010) http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/default.aspx
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Xiao, X., Zheng, Y., Luo, Q. et al. Inferring social ties between users with human location history. J Ambient Intell Human Comput 5, 3–19 (2014). https://doi.org/10.1007/s12652-012-0117-z
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DOI: https://doi.org/10.1007/s12652-012-0117-z