TY - GEN
T1 - Follow you from your photos
AU - Zhang, Jie
AU - Zhao, Hui
AU - Xie, Yusheng
PY - 2013
Y1 - 2013
N2 - In this work, we focus on the travelling location prediction problem of detecting whether a person will leave his living area and where he will go by analyzing the hidden connection between the user behaviors on geography and online social interactions. By analyzing more than 40, 000 Instagram media records from 26, 000 users, spanning a period of 3 months, we give special consideration to rarely visits locations, which are often ignored as noise in previous works, and we employ the dynamic Bayesian network to estimate the users' behavior and predict the location according to a majority voting model based on the social interaction information. We compare our model on the data of Instagram with two existing location prediction models, and find that (1) our model performs well both in the general location prediction and the location outside the living area.(2) social ties are effective for solving the location prediction problem as the accuracy of the prediction gets higher, given more social interaction information.
AB - In this work, we focus on the travelling location prediction problem of detecting whether a person will leave his living area and where he will go by analyzing the hidden connection between the user behaviors on geography and online social interactions. By analyzing more than 40, 000 Instagram media records from 26, 000 users, spanning a period of 3 months, we give special consideration to rarely visits locations, which are often ignored as noise in previous works, and we employ the dynamic Bayesian network to estimate the users' behavior and predict the location according to a majority voting model based on the social interaction information. We compare our model on the data of Instagram with two existing location prediction models, and find that (1) our model performs well both in the general location prediction and the location outside the living area.(2) social ties are effective for solving the location prediction problem as the accuracy of the prediction gets higher, given more social interaction information.
KW - Dynamic Bayesian network
KW - Instagram
KW - Location prediction
KW - Majority voting
KW - Social interaction
UR - https://www.scopus.com/pages/publications/84893484711
U2 - 10.1109/GreenCom-iThings-CPSCom.2013.169
DO - 10.1109/GreenCom-iThings-CPSCom.2013.169
M3 - 会议稿件
AN - SCOPUS:84893484711
SN - 9780769550466
T3 - Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
SP - 985
EP - 992
BT - Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
T2 - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
Y2 - 20 August 2013 through 23 August 2013
ER -