document: Exploiting place features in link prediction on location-based social networks Link prediction systems have been largely adopted to recommend new friends in online social networks using data about social interactions . With the soaring adoption of location-based social services it becomes possible to take advantage of an additional source of information : the places people visit . In this paper we study the problem of designing a link prediction system for online location-based social networks . We have gathered extensive data about one of these services , Gowalla , with periodic snapshots to capture its temporal evolution . We study the link prediction space , finding that about 30 % of new links are added among `` place-friends '' , i.e. , among users who visit the same places . We show how this prediction space can be made 15 times smaller , while still 66 % of future connections can be discovered . Thus , we define new prediction features based on the properties of the places visited by users which are able to discriminate potential future links among them . Building on these findings , we describe a supervised learning framework which exploits these prediction features to predict new links among friends-of-friends and place-friends . Our evaluation shows how the inclusion of information about places and related user activity offers high link prediction performance . These results open new directions for real-world link recommendation systems on location-based social networks .
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