Mining Behavioural Patterns in Urban Mobility Sequences Using Foursquare Check-in Data from Tokyo
暂无分享,去创建一个
[1] Thomas V. Pollet,et al. Birds of a feather locate together? Foursquare checkins and personality homophily , 2016, Comput. Hum. Behav..
[2] Mason A. Porter,et al. Random walks and diffusion on networks , 2016, ArXiv.
[3] James B. D. Joshi,et al. Exploring trajectory-driven local geographic topics in foursquare , 2012, UbiComp.
[4] Fabio Pianese,et al. Discovering and predicting user routines by differential analysis of social network traces , 2013, 2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).
[5] Cecilia Mascolo,et al. A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.
[6] Jochen De Weerdt,et al. Mining Behavioral Sequence Constraints for Classification , 2020, IEEE Transactions on Knowledge and Data Engineering.
[7] Cecilia Mascolo,et al. A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.
[8] Pablo Martí,et al. Social Media data: Challenges, opportunities and limitations in urban studies , 2019, Comput. Environ. Urban Syst..
[9] Tian Lan,et al. Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies , 2014 .
[10] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[11] Wil M. P. van der Aalst,et al. DECLARE: Full Support for Loosely-Structured Processes , 2007, 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007).
[12] Albert-László Barabási,et al. Understanding individual human mobility patterns , 2008, Nature.