Exploring trajectory-driven local geographic topics in foursquare

UbiComp'12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Volumn , Issue , 2012, Pages 927-934

  Long, Xuelian ^a   Jin, Lei ^a   Joshi, James ^a  

^a UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Foursquare; Geographic Topics; Location based Social Networking Services; Trajectory

Indexed keywords

LOCATION; SOCIAL NETWORKING (ONLINE); STATISTICS; TRAJECTORIES; UBIQUITOUS COMPUTING;

FOURSQUARE; FRIEND RECOMMENDATIONS; GEOGRAPHIC TOPICS; HUMAN MOBILITY; INTRINSIC RELATION; LATENT DIRICHLET ALLOCATIONS; LOCATION BASED; SOCIAL NETWORKING SERVICES;

LOCATION BASED SERVICES;

EID: 84879484848     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2370216.2370423     Document Type: Conference Paper

References (17)

1. Zheng, Y. Location-based social networks: Users. Computing with Spatial Trajectories, Zheng, Y and Zhou, X., Eds. Springer, 2011.
2. Foursquare Nears 20 Million Users And Crowley Talks About His Co-founder's Recent Departure. http://articles.businessinsider.com/ 2012-03-10/tech/31142426-1- foursquare-sxsw-dennis-crowley.
3. 200M users include location in Facebook posts; company looks to expand location APIs. http://www.insidefacebook.com/2012/04/05/ 200m-users-include- location-in-facebook-posts -company-looks-to-expand-location-apis/.
4. Get tickets for select penguins home games with american eagle student rush. http://www.ticketmaster.com/promo/u3c7b3? brand=penguins.
5. Noulas, A., Scellato, S., Mascolo, C. and Pontil, M. Empirical study of geographic user activity patterns in foursquare. In ICWSM'11, 2011.
6. Blei, D.M., Ng, A.Y., and Jordan, M.I. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
7. Chang, J. and Sun, E. Location3: How users share and respond to location-based data on social. In ICWSM'11, 2011.
8. Cranshaw, J., Schwartz, R., Hong, J.I. and Sadeh, N. The livehoods project: Utilizing social media to understand the dynamics of a city. In ICWSM'12, 2012.
9. Cranshaw, J. and Yano, T. Seeing a home away from the home: Distilling proto-neighborhoods from incidental data with latent topic modeling. In CSSWC Workshop at NIPS'10, 2010.
10. Yuan, J., Zheng, Y. and Xie, X. Discovering regions of different functions in a city using human mobility and pois. In KDD'12, 2012.
11. Farrahi, K. and Gatica-Perez, D. Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology, 2(1), 2011.
12. Ferrari, L., Rosi, A., Mamei, M. and Zambonelli, F. Extracting urban patterns from location-based social networks. In LBSN'11, 2011.
13. Ferrari, L. and Mamei, M. Discovering daily routines from google latitude with topic models. In CoMoRea'11, 2011.
14. Eagle, N., Pentland, A. and Lazer, D. Inferring social network structure using mobile phone data. In Proceedings of the National Academy of Sciences (PNAS), volume 106, pages 15274-15278, 2009.
15. Li, N. and Chen, G. Analysis of a location-based social network. In CSE'09, 2009.
16. Griffiths, T. and Steyvers, M. Finding scientific topics. Proceedings of the National Academy of Sciences, 101(supple.1):5228-5235, 2004.
17. Cheng, Z., Caverlee, J., Lee, K. and Sui, D.Z. Exploring millions of footprints in location sharing services. In ICWSM'11, 2011.