Exploring Communities for Effective Location Prediction

Jun Pang, Yang Zhang

Abstract: Humans are social animals, they interact with different communities to conduct different activities. The literature has shown that human mobility is constrained by their social relations. In this work, we investigate the social impact on a user's mobility from his communities in order to conduct location prediction effectively. Through analysis of a real-life dataset, we demonstrate that (1) a user gets more influences from his communities than from all his friends; (2) his mobility is influenced only by a small subset of his communities; (3) influence from communities depends on social contexts. We further exploit a SVM to predict a user's future location based on his community information. Experimental results show that the model based on communities leads to more effective predictions than the one based on friends.