| With the rapid growth of location-based social networks (LBSNs), it is now available to analyze and understand user mobility behavior in real world. Studies show that users usually visit nearby points of interest (POIs), located in small regions, especially when they travel out of their hometowns. However, previous out-of-town recommendation systems mainly focus on recommending individual POIs that may reside far from each other, which makes the recommendation results less useful. In this paper, we introduce a novel problem called Region Recommendation, which aims to recommend an out-of-town region of POIs that are likely to be visited by a user. The proximity characteristic of user mobility behavior implies that the probability of visiting one POI depends on those of nearby POIs. Thus, to make accurate region recommendation, our proposed model exploits the influence between POIs, instead of treating them individually. Moreover, to overcome the efficiency problem of searching the best region, we propose a sweeping line-based method, and subsequently a constant-bounded algorithm for better efficiency. Experiments on two real-world datasets demonstrate the improved effectiveness of our models over baseline methods and efficiency of the approximate algorithm. |
