DOI 10.1007/s11704-013-3902-8 Co-occurrence prediction in a large location-based social network

Abstract Location-based social network (LBSN) is at the forefront of emerging trends in social network services (SNS) since the users in LBSN are allowed to “check-in ” the places (locations) when they visit them. The accurate geographi-cal and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co-occurrences and social ties, and the results show that the co-occurrences are strongly correlative with the social ties. Sec-ond, we present a study of predicting two users whether or not they will meet (co-occur) at a place in a given future time, by exploring their check-in habits. In particular, we first intro-duce two new concepts, bag-of-location and bag-of-time-lag, to characterize user’s check-in habits. Based on such bag rep-resentations, we define a similarity metric called habits sim-ilarity to measure the similarity between two users ’ check-in habits. Then we propose a machine learning formula for pre-dicting co-occurrence based on the social ties and habits sim-ilarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method