Place-Friends: designing a link prediction system for location-based services

Online social networks often deploy friend recommending systems, so that new users can be discovered and new social connections can be created. Since these service have easily millions of users, recommending friends potentially involves searching a huge prediction space: this is why platforms such as Facebook, LinkedIn and Twitter merely focus their prediction efforts on friends-of-friends, that is, on users that are only 2 hops away in the social network, sharing at least a common friend. Extending prediction efforts beyond this social circle is simply not worth it.

Nonetheless, in location-based social networks there is an unprecedented source of potential promising candidates for recommending new friends: the places where user check-in at.  In a recent paper which will appear at the upcoming ACM SIGKDD 2011 conference we address the problem of designing a link prediction system which exploits the properties of the places that users visit.