TY - GEN
T1 - Talents Recommendation with Multi-Aspect Preference Learning
AU - Yi, Fei
AU - Yu, Zhiwen
AU - Xu, Huang
AU - Guo, Bin
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Discovering talents has always been a crucial mission in recruitment and applicant selection program. Traditionally, hunting and identifying the best candidate for a particular job is executed by specialists in human resources department, which requires complex manual data collection and analysis. In this paper, we propose to seek talents for companies by leveraging a variety of data from not only online professional networks (e.g., LinkedIn), but also other popular social networks (e.g., Foursquare and Last.fm). Specifically, we extract three distinct features, namely global, user and job preference to understand the patterns of talent recruitment, and then a Multi-Aspect Preference Learning (MAPL) model for applicant recommendation is proposed. Experimental results based on a real-world dataset validate the effectiveness and usability of our proposed method, which can achieve nearly 75% accuracy at best in recommending candidates for job positions.
AB - Discovering talents has always been a crucial mission in recruitment and applicant selection program. Traditionally, hunting and identifying the best candidate for a particular job is executed by specialists in human resources department, which requires complex manual data collection and analysis. In this paper, we propose to seek talents for companies by leveraging a variety of data from not only online professional networks (e.g., LinkedIn), but also other popular social networks (e.g., Foursquare and Last.fm). Specifically, we extract three distinct features, namely global, user and job preference to understand the patterns of talent recruitment, and then a Multi-Aspect Preference Learning (MAPL) model for applicant recommendation is proposed. Experimental results based on a real-world dataset validate the effectiveness and usability of our proposed method, which can achieve nearly 75% accuracy at best in recommending candidates for job positions.
KW - Multi-Aspects Preference Learning
KW - Talent recommendation
UR - http://www.scopus.com/inward/record.url?scp=85064056371&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15093-8_29
DO - 10.1007/978-3-030-15093-8_29
M3 - 会议稿件
AN - SCOPUS:85064056371
SN - 9783030150921
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 409
EP - 423
BT - Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers
A2 - Li, Shijian
PB - Springer Verlag
T2 - 13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018
Y2 - 11 May 2018 through 13 May 2018
ER -