TY - GEN
T1 - Towards Diversified Local Users Identification Using Location Based Social Networks
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 Association for Computing Machinery.
PY - 2017/7/31
Y1 - 2017/7/31
N2 - Identifying a set of diversified users who are local residents in a city is an important task for a wide spectrum of applications such as target ads of local business, surveys and interviews, and personalized recommendations. While many previous studies have investigated the problem of identifying the local users in a given area using online social network information (e.g., geotagged posts), few methods have been developed to solve the diversified user identification problem. In this paper, we propose a new analytical framework, Diversified Local Users Finder (DLUF), to accurately identify a set of diversified local users using a principled approach. In particular, the DLUF scheme first defines a new distance metric that measures the diversity between local users from physical dimension. The DLUF scheme then provides a solution to find the set of local users with maximum diversity. The performance of DLUF scheme is compared to several representative baselines using two real world datasets obtained from Foursquare application. We observe that the DLUF scheme accurately identifies the local users with a great diversity and significantly outperforms the compared baselines.
AB - Identifying a set of diversified users who are local residents in a city is an important task for a wide spectrum of applications such as target ads of local business, surveys and interviews, and personalized recommendations. While many previous studies have investigated the problem of identifying the local users in a given area using online social network information (e.g., geotagged posts), few methods have been developed to solve the diversified user identification problem. In this paper, we propose a new analytical framework, Diversified Local Users Finder (DLUF), to accurately identify a set of diversified local users using a principled approach. In particular, the DLUF scheme first defines a new distance metric that measures the diversity between local users from physical dimension. The DLUF scheme then provides a solution to find the set of local users with maximum diversity. The performance of DLUF scheme is compared to several representative baselines using two real world datasets obtained from Foursquare application. We observe that the DLUF scheme accurately identifies the local users with a great diversity and significantly outperforms the compared baselines.
KW - Diversified local users
KW - Location based social networks foursquare
UR - http://www.scopus.com/inward/record.url?scp=85040244658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040244658&partnerID=8YFLogxK
U2 - 10.1145/3110025.3110159
DO - 10.1145/3110025.3110159
M3 - Conference contribution
AN - SCOPUS:85040244658
T3 - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
SP - 115
EP - 118
BT - Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
A2 - Diesner, Jana
A2 - Ferrari, Elena
A2 - Xu, Guandong
PB - Association for Computing Machinery
T2 - 9th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017
Y2 - 31 July 2017 through 3 August 2017
ER -