Point-of-interest recommendation based on the user check-in behavior

With the rapid development of big data technology, recommendation systems become an important research direction in the field of big data. With the rapid development of location-based networks, point-of-interest (POI) recommendation has become an important way to help people discover interesting and attractive locations, especially when users travel out of town. However, because users check-in interaction is highly sparse, which brings a big challenge for POI recommendation. To tackle this challenge, a growing line of researches have exploited the geographical influence, temporal effect, social correlation, content information and popularity impact. However, current research lacks an integrated analysis of the joint effect of the above factors to deal with the problem of data sparsity, especially in the out-of-town recommendation scenario which has been ignored by most existing work. In light of the above, we propose a joint probabilistic generative model called GTSCP to imitate user check-in activities in a process of setting decision, which integrates the above factors to overcome the data sparsity effectively, especially for out-of-town users. The POI recommendation method consists of offline modeling and online recommendation. The GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. We implement experiments on real LBSNs check-in datasets, experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques. © 2017, Science Press. All right reserved.