ShopProfiler: Profiling shops with crowdsourcing data

Xiaonan Guo, Eddie C.L. Chan, Ce Liu, Kaishun Wu, Siyuan Liu, Lionel M. Ni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations


Sensing data from mobile phones provide us exciting and profitable applications. Recent research focuses on sensing indoor environment, but suffers from inaccuracy because of the limited reachability of human traces or requires human intervention to perform sophisticated tasks. In this paper, we present ShopProfiler, a shop profiling system on crowdsourcing data. First, we extract customer movement patterns from traces. Second, we improve accuracy of building floor plan by adopting a gradient-based approach and then localize shops through WiFi heat map. Third, we categorize shops by designing an SVM classifier in shop space to support multi-label classification. Finally, we infer brand name from SSID by applying string similarity measurement. Based on over five thousand traces in three big malls in two different countries, we conclude that ShopProfiler achieves better accuracy in building refined floor plan, and characterizes shops in terms of location, category and name with little human intervention.

Original languageEnglish (US)
Title of host publicationIEEE INFOCOM 2014 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
ISBN (Print)9781479933600
StatePublished - 2014
Event33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014 - Toronto, ON, Canada
Duration: Apr 27 2014May 2 2014

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Other33rd IEEE Conference on Computer Communications, IEEE INFOCOM 2014
CityToronto, ON

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering


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