TY - GEN
T1 - Can your friends predict where you will be?
AU - Cao, Lei
AU - She, James
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/3/12
Y1 - 2014/3/12
N2 - 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 mostly 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.
AB - 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 mostly 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.
KW - Big data
KW - Location prediction
KW - Social network analysis
UR - http://www.scopus.com/inward/record.url?scp=84946690285&partnerID=8YFLogxK
U2 - 10.1109/iThings.2014.80
DO - 10.1109/iThings.2014.80
M3 - Conference contribution
AN - SCOPUS:84946690285
T3 - Proceedings - 2014 IEEE International Conference on Internet of Things, iThings 2014, 2014 IEEE International Conference on Green Computing and Communications, GreenCom 2014 and 2014 IEEE International Conference on Cyber-Physical-Social Computing, CPS 2014
SP - 450
EP - 455
BT - Proceedings - 2014 IEEE International Conference on Internet of Things, iThings 2014, 2014 IEEE International Conference on Green Computing and Communications, GreenCom 2014 and 2014 IEEE International Conference on Cyber-Physical-Social Computing, CPS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Internet of Things, iThings 2014, Collocated with 2014 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2014 and 2014 IEEE International Conference on Green Computing and Communications, GreenCom 2014
Y2 - 1 September 2014 through 3 September 2014
ER -