Abstract
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.) subnetworks. However, previous’s work models were static, failing to capture adequately user preferences as they change over time. In this paper, we provide a novel recommendation method by incorporating the time dimension into our model through an auxiliary artificial node (i.e. session). In particular, we construct a hybrid tripartite (i.e., user, location, session) graph, which incorporates 7 different unipartite and bipartite graphs. Then, we run on it the well known Random Walk with Restart (RWR) algorithm, which randomly propagate through the network structure which has 7 differently weighted edge types (i.e., user-location, user-session, user-user, etc.) among its entities. We evaluate experimentally how RWR improve the procession of the recommendations during different time-windows against one state-of-the-art algorithm over the GeoSocialRec and the Foursquare datasets.