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

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.