Weighted hierarchical geographic information description model for social relation estimation

Social relation estimation has been attracting researchers' attention worldwide, and rapid development of LBSN (Location-Based Social Network) provides researchers an additional resource to estimate users' social relations. Previous works have fulfilled the social relation estimation with spatial information extracted from LBSN, while ignored or paid a little attention to the property of location. In this paper, a hierarchical grid based method is proposed to define location ID, and location's property is taken full advantage of when extracting features, in which way to exploit users' spatial information more sufficiently. Besides, in order to train a robust estimation model, we design the model based on semi-supervised learning. Our careful consideration of the above issues ultimately leads to a general framework that outperforms competitors, and experiments prove the effectiveness finally.