May 01, 2017

Extracting Mobile User Behavioral Similarity via Cell-Level Location Trace

  • Cao J.
  • Chen S.
  • Kennedy W.
  • Kim N.
  • Zhang Y.

We study mobile user behavior from cell-level location trace (CLLT). Since CLLT contains no GPS coordinates of mobile users, we infer approximate user locations from the cell locations they visit. We build upon the vast literature on user behavior analysis and demonstrate the ability to extract user behavior in the absence of the more precise GPS information. We focus on the ``leisure time'' behavior, i.e. activities outside home and office. In particular, we compare pairs of users and study their similarity or the lack of it. Such similarity comparison can be done directly using the users' actual cell-level locations and the times of their visits. We observe that a user's behavior on different days tends to be more similar to oneself than to others. We then compare users in terms of their activities irrespective of physical locations. We develop the notion of {em semantic cell type} which classifies the cells according to the consistency of points of interest within the cells. In this way, we can compare two users based on the type of cells they visit and extract similarity from there. As a result, from our understanding of the general profiles of the cells and the users, we are able to differentiate user behavior and cluster them in a meaningful way.

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© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Many of today's websites have recognised the importance of mobile friendly pages to keep users engaged and to provide a satisfying user experience. However, next to the experience provided by the sites themselves, ...

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© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Uber is a popular ride-sharing application that matches people who need a ride (or riders) with drivers who are willing to provide it using their personal vehicles. Despite its growing popularity, there exist ...

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© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel ...