Mobile and Ubiquitous Systems: Computing, Networking, and Services. 9th International Conference, MobiQuitous 2012, Beijing, China, December 12-14, 2012. Revised Selected Papers

Research Article

GroupMe: Supporting Group Formation with Mobile Sensing and Social Graph Mining

Download164 downloads
  • @INPROCEEDINGS{10.1007/978-3-642-40238-8_17,
        author={Bin Guo and Huilei He and Zhiwen Yu and Daqing Zhang and Xingshe Zhou},
        title={GroupMe: Supporting Group Formation with Mobile Sensing and Social Graph Mining},
        proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 9th International Conference, MobiQuitous 2012, Beijing, China, December 12-14, 2012. Revised Selected Papers},
        proceedings_a={MOBIQUITOUS},
        year={2013},
        month={9},
        keywords={Social graph mining context-awareness group formation and recommendation mobile sensing social activity organization},
        doi={10.1007/978-3-642-40238-8_17}
    }
    
  • Bin Guo
    Huilei He
    Zhiwen Yu
    Daqing Zhang
    Xingshe Zhou
    Year: 2013
    GroupMe: Supporting Group Formation with Mobile Sensing and Social Graph Mining
    MOBIQUITOUS
    Springer
    DOI: 10.1007/978-3-642-40238-8_17
Bin Guo1,*, Huilei He1, Zhiwen Yu1, Daqing Zhang1, Xingshe Zhou1
  • 1: Northwestern Polytechnical University
*Contact email: guobin.keio@gmail.com

Abstract

Nowadays, social activities in the real world (e.g., meetings, discussions, parties) are more and more popular and important to human life. As the number of contacts increases, the implicit social graph becomes increasingly complex, leading to a high cost on social activity organization and activity group formation. In order to promote the interaction among people and improve the efficiency of social activity organization, we propose a mobile social activity support system called GroupMe, which facilitates the activity group initiation based on mobile sensing and social graph mining. In GroupMe, user activities are automatically sensed and logged in the social activity logging (ACL) repository. By analyzing the historical ACL data through a series of group mining (group extraction, group abstraction) algorithms, we obtain implicit logical contact groups. We then use the sensed contexts and the computed user affinity to her logical groups to suggest highly relevant groups in social activity initiation. The experimental results verify the effectiveness of the proposed approach.