Target and Non-target Category Classification from GPS and Check-in Data

GPS data analysis is one of the main operators in geographical information systems.However, because of security and privacy issues, we often face situations where GPS data cannot be obtained frequently.Such situations and the measurement errors of GPS coordinates make identifying user behaviors challenging.In this work, we assume this setting and tackle the classification problem of target and non-target categories for the first time.Target categories are store categories in the scope of a service provider, whereas nontarget ones are those that are not in.Given a GPS point, this problem estimates which category this location belongs to, so it is a binary classification problem.This problem has two main difficulties.First, we cannot obtain labeled data of the non-target categories.Second, many GPS data have error ranges and no labels, i.e., they do not clarify where the users stay.To solve the problem while addressing these difficulties, we propose a new classification method based on machine learning.We exploit GPS and check-in data to obtain user feature vectors at a given time.Our loss function considers nonstay information on each store category to identify the non-target space in the feature space.From these techniques, we compute the probability of staying in one of the (non-)target categories.We conduct experiments on real-world datasets, and the results show the effectiveness of our method. CCS Concepts