In recent years, researches on the mining of user check-in behaviors for point-of-interest(POI) recommendations has attracted a lot of attention. Personalized POI recommendation is a significant task in location-based social networks(LBSNs) because it helps target users explore their surrounding environment and greatly benefits the business in real life. Although a personalized POI recommendation system can significantly facilitate users’ outdoor activities, it faces many challenging problems, such as the hardness to model human mobility and the difficulty to address data sparsity. Moreover, geographical influence on users should be personalized, but current studies only model the geographical influence on all users’ check-in behaviors in a universal way. In this paper, we design a novel and effective personalized POI recommendation system. First, our system mines the target user’s active area based on his or her check-in history, and designs a personalized user spatial similarity calculation method based on the target user’s active area. Secondly, our system takes into account three features of the human mobility pattern: spatial, temporal, and sequential properties. Furthermore, our system designs a novel personalized user mobility pattern similarity calculation method based on the features of human mobility pattern. Finally, a recommendation list is generated based on the idea of collaborative filtering. Compared with the state-of-the-art POI recommendation approaches, the experimental results demonstrate that our system achieves much better performance.

Introduction

With the rapid emergence of location-aware social media, location-based social networks (LBSNs) as shown in Figure 1 are becoming more and more popular with users. LBSNs enable users to easily share content associated with locations. There are numerous types of popular LBSNs. One type of LBSNs represented by Foursquare and Gowalla mainly provides check-in services that attract millions of users to check in their favorite POIs and share their experience in accessing these POIs with friends. Through in-depth understanding of LBSNs, it can be found that LBSNs are heterogeneous networks, in which there are two nodes with different attributes, namely, location nodes and user nodes. According to these two kinds of nodes, there are three kinds of relationships among LBSNs: locationto-location relationship, user-to-user relationship, locationto-user relationship. And there are three different distances corresponding to the three relationships: the distance between the two locations, the distance between the two users(refers to the geographical distance between the current location of two users), and the distance between a user and a location(refers to the geographical distance between a user’s current location and a location). In LBSNs, the distance between two locations directly reflects the degree of correlation between the two locations. For example, many shopping malls are adjacent to each other to form a commercial center. The distance between two users can reflect the similarity between the two users. For example, multiple POIs in each user’s travel trajectory are relatively close, that is, the trajectories of the two users are similar, indicating that the two users have similar preference or mobility pattern.