Recommender systems in location-based social networks (LBSNs), such as Facebook Places and Foursquare, have focused on recommending friends or locations to registered users by combining information derived from explicit (i.e. friendship network) and implicit (i.e. user-item rating network, user-location network, etc.) sub-networks. However, previous models were static and failed to adequately capture user time-varying preferences. In this paper, we provide a novel recommendation method based on the time dimension as well. We construct a hybrid tripartite (i.e., user, location, session) graph, which incorporates 7 different unipartite and bipartite graphs. Then, we test it with an extended version of the Random Walk with Restart (RWR) algorithm, which randomly walks through the network by using paths of 7 differently weighted edge types (i.e., user-location, user-session, user-user, etc.). We evaluate experimentally our method and compare it against three state-of-the-art algorithms on two real-life datasets; we show a significant prevalence of our method over its competitors.