Mining User Mobility Features for Next Place Prediction in Location-Based Services
Mobile location-based services are thriving, providing an unprecedented opportunity to collect fine grained spatiotemporal data about the places users visit. This multi-dimensional source of data offers new possibilities to tackle established research problems on human mobility, but it also opens avenues for the development of novel mobile applications and services. In this paper, the authors study the problem of predicting the next venue a mobile user will visit, by exploring the predictive power offered by different facets of user behavior. They first analyze about 35 million check-ins made by about 1 million Foursquare users in over 5 million venues across the globe, spanning a period of five months.