Article: Automatic identification of informal social groups and places for geo-social recommendations Journal: International Journal of Mobile Network Design and Innovation (IJMNDI) 2007 Vol.2 No.3/4 pp.159 - 171 Abstract: Mobile locatable devices can help identify previously unknown ad hoc or semi-permanent groups of people and their meeting places. Newly identified groups or places can be recommended to people to enhance their geo-social experience, while respecting privacy constraints. For instance, new students can learn about popular hangouts on campus or faculty members can learn about groups of students routinely having research discussions. This paper presents a clustering algorithm based on user copresence that identifies such groups and places even when group members participate to only a certain fraction of meetings. Simulation results demonstrate that 90–96% of group members can be identified with negligible false positives when the user meeting attendance is at least 50%. Experimental results using one-month of mobility traces collected from smart phones running Intel|s PlaceLab location engine successfully identified all groups that met regularly during that period. Additionally, the group places were identified with good accuracy. Inderscience Publishers - linking academia, business and industry through research

Title: Automatic identification of informal social groups and places for geo-social recommendations

Authors: Ankur Gupta, Sanil Paul, Quentin Jones, Cristian Borcea

Addresses: Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA. ' Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA. ' Department of Information Systems, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA. ' Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA

Abstract: Mobile locatable devices can help identify previously unknown ad hoc or semi-permanent groups of people and their meeting places. Newly identified groups or places can be recommended to people to enhance their geo-social experience, while respecting privacy constraints. For instance, new students can learn about popular hangouts on campus or faculty members can learn about groups of students routinely having research discussions. This paper presents a clustering algorithm based on user copresence that identifies such groups and places even when group members participate to only a certain fraction of meetings. Simulation results demonstrate that 90–96% of group members can be identified with negligible false positives when the user meeting attendance is at least 50%. Experimental results using one-month of mobility traces collected from smart phones running Intel|s PlaceLab location engine successfully identified all groups that met regularly during that period. Additionally, the group places were identified with good accuracy.

Keywords: mobile social computing; location aware recommender systems; group identification; place identification; informal social groups; geo-social recommendations; privacy constraints; clustering algorithms.

DOI: 10.1504/IJMNDI.2007.017320

International Journal of Mobile Network Design and Innovation, 2007 Vol.2 No.3/4, pp.159 - 171

Published online: 26 Feb 2008 *

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Title: Automatic identification of informal social groups and places for geo-social recommendations

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