TY - GEN
T1 - Where Are You From
T2 - 2017 IEEE Conference on Computer Communications, INFOCOM 2017
AU - Huang, Chao
AU - Wang, Dong
AU - Zhu, Shenglong
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. CBET-1637251, CNS-1566465 and IIS-1447795 and Army Research Office under Grant W911NF-16-1-0388. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/2
Y1 - 2017/10/2
N2 - Crowdsourcing has emerged as an important data collection paradigm in participatory and human-centric sensing applications. While many crowdsourcing studies focus on sensing and recovering the status of the physical world, this paper investigates the problem of profiling the crowd sensors (i.e., humans). In particular, we study the problem of accurately inferring the home locations of people from the noisy and sparse crowdsourcing data they contribute. In this study, we propose a semi-supervised framework, Where Are You From (WAYF), to accurately infer the home locations of people by explicitly exploring the localness of people and the dependency between people based on their check-in behaviors under a rigorous analytical framework. We perform extensive experiments to evaluate the performance of our scheme and compared it to the state-of-the-art techniques using three real world data traces collected from Foursquare. The results showed the effectiveness of our scheme in accurately profiling the home locations of people.
AB - Crowdsourcing has emerged as an important data collection paradigm in participatory and human-centric sensing applications. While many crowdsourcing studies focus on sensing and recovering the status of the physical world, this paper investigates the problem of profiling the crowd sensors (i.e., humans). In particular, we study the problem of accurately inferring the home locations of people from the noisy and sparse crowdsourcing data they contribute. In this study, we propose a semi-supervised framework, Where Are You From (WAYF), to accurately infer the home locations of people by explicitly exploring the localness of people and the dependency between people based on their check-in behaviors under a rigorous analytical framework. We perform extensive experiments to evaluate the performance of our scheme and compared it to the state-of-the-art techniques using three real world data traces collected from Foursquare. The results showed the effectiveness of our scheme in accurately profiling the home locations of people.
KW - Crowdsourcing
KW - Home Location Profiling
KW - Location Based Social Networks (LBSN)
UR - http://www.scopus.com/inward/record.url?scp=85021301276&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021301276&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2017.8057112
DO - 10.1109/INFOCOM.2017.8057112
M3 - Conference contribution
AN - SCOPUS:85021301276
T3 - Proceedings - IEEE INFOCOM
BT - INFOCOM 2017 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 May 2017 through 4 May 2017
ER -