Can Your Friends Predict Where You Will Be?

Lei Cao, James She
2014 2014 IEEE International Conference on Internet of Things(iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom)  
With the development of mobile device and wireless networks, user location becomes increasingly valuable in enhancing user experience, system performance and resource allocation. Location-based services have been not only an important perspective of social media, but also a significant contributor to big data analysis. Location prediction, as an interesting topic, can help improve system performance and user experience in location-based services. Existing algorithms on such prediction focus
more » ... ly on exploring regularity in users' movement history without taking advantage of the research on social networks, which can provide information on other factors such as peer influence in human mobility. In this work, the aim is to propose an enhanced location prediction model based on both users' mobility patterns and social network information and the proposed algorithm shows a significant improvement over existing ones.
doi:10.1109/ithings.2014.80 dblp:conf/ithings/CaoS14 fatcat:mh4irztehvfi5liosx3htmesxe