  Foursquare is an online social network and can be represented with a
bipartite network of users and venues. A user-venue pair is connected if a user
has checked-in at that venue. In the case of Foursquare, network analysis
techniques can be used to enhance the user experience. One such technique is
link prediction, which can be used to build a personalized recommendation
system of venues. Recommendation systems in bipartite networks are very often
designed using the global ranking method and collaborative filtering. A less
known method- network based inference is also a feasible choice for link
prediction in bipartite networks and sometimes performs better than the
previous two. In this paper we test these techniques on the Foursquare network.
The best technique proves to be the network based inference. We also show that
taking into account the available metadata can be beneficial.
