Abstract
The large volume of data available in location-based social networks (LBSNs) enables Point-of-Interest (POI) recommendation services. On another hand, the heterogeneous information (e.g., user check-in records, geographical features of POIs, social network and user reviews) imposes great challenges on effective POI recommendation. In this paper, we focus on leveraging such rich information in an integrated manner to improve POI recommendation performance. We exploit not only the geographical and social information, but also aspects extracted from user reviews to better model users' preferences. More specifically, to fully utilize various types of information, we construct a novel heterogeneous graph, Aspect-aware Geo-Social influence Graph (AGSG), by fusing various relations among the three types of nodes, i.e., users, POIs and aspects. The personalized POI recommendation task is then transformed as a graph node ranking problem in AGSG. We design a graph-based recommendation algorithm based on both personalized PageRank (PPR) and meta paths, to fully exploit the heterogeneous graph structure as well as to capture the semantic relations among the various nodes. Experiments on three real-world datasets show that our proposed approach outperforms the state-of-art methods.