TY - GEN
T1 - What Is my next job
T2 - Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
AU - Qu, Hongyang
AU - Yu, Zhiwen
AU - Yu, Zhiyong
AU - Xu, Huang
AU - Guo, Bin
AU - Xie, Xing
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - Job mobility is common in modern society, especially in the industry of information and communication technology. Job mobility prediction is valuable both for employees and employers. For the sake of lacking appropriate and sufficient records of job mobility, the traditional methods meet a significant challenge in job mobility prediction. Fortunately, the emerging professional social network provides a large amount of users' career histories, which can alleviate this problem. In this paper, we collect relevant data from LinkedIn, and analyze the temporal and spatial characteristics to model the job mobility pattern. We propose an approach to predict the company size and position for the next job by using various features, such as the position, duration, and size of previous companies, education degree, etc. The experimental results verify the proposed approach with the accuracy up to 72% and 74% in terms of next company size prediction and next position prediction respectively.
AB - Job mobility is common in modern society, especially in the industry of information and communication technology. Job mobility prediction is valuable both for employees and employers. For the sake of lacking appropriate and sufficient records of job mobility, the traditional methods meet a significant challenge in job mobility prediction. Fortunately, the emerging professional social network provides a large amount of users' career histories, which can alleviate this problem. In this paper, we collect relevant data from LinkedIn, and analyze the temporal and spatial characteristics to model the job mobility pattern. We propose an approach to predict the company size and position for the next job by using various features, such as the position, duration, and size of previous companies, education degree, etc. The experimental results verify the proposed approach with the accuracy up to 72% and 74% in terms of next company size prediction and next position prediction respectively.
KW - Job mobility prediction
KW - Professional social networks
KW - Temporal and spatial characteristics
UR - http://www.scopus.com/inward/record.url?scp=85015156936&partnerID=8YFLogxK
U2 - 10.1109/TrustCom.2016.0256
DO - 10.1109/TrustCom.2016.0256
M3 - 会议稿件
AN - SCOPUS:85015156936
T3 - Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
SP - 1668
EP - 1675
BT - Proceedings - 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on Parallel and Distributed Processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 August 2016 through 26 August 2016
ER -