Are you on the right track? Learning career tracks for job movement analysis

Career track represents a vertical career pathway, where one can gradually move up to take up higher job appointments when relevant skills are acquired. Understanding the propensity of career movements in an evolving job market can enable timely career guidance to job seekers and working professiona...

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Main Authors: CHIANG, Meng-Fen, LIM, Ee-peng, LEE, Wang-Chien, TIAN, Yuan, HUNG, Chih-Chieh
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4259
https://ink.library.smu.edu.sg/context/sis_research/article/5262/viewcontent/dshcm_2018_paper_6_career_tracks.pdf
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.sis_research-52622019-05-03T03:36:38Z Are you on the right track? Learning career tracks for job movement analysis CHIANG, Meng-Fen LIM, Ee-peng LEE, Wang-Chien TIAN, Yuan HUNG, Chih-Chieh Career track represents a vertical career pathway, where one can gradually move up to take up higher job appointments when relevant skills are acquired. Understanding the propensity of career movements in an evolving job market can enable timely career guidance to job seekers and working professionals. To this end, we harvest career trajectories from online professional network (OPN). Our focus lies on obtaining a macro view on career movements at the track granularity. Specifically, we propose a semi-supervised career track labelling framework to automatically assign career tracks for large set of jobs. To contextually label jobs, we collect example jobs with career track labels identified by human resource specialists and domain experts in Singapore. An intuitive idea is to learn the labelling knowledge from the example jobs and then apply to jobs in OPN. Unfortunately, such small amount of labeled jobs presents a great challenge in our attempt to accurately recover career tracks for plentiful unlabelled jobs. We thus address the issue by resorting to semi-supervised learning methods. This research not only reduces the human annotation efforts in maintaining the career track knowledge databases over time across different geographical regions, but also facilitates data science study on career movements. Extensive experiments are conducted to demonstrate the labelling accuracy as well as to gain insights upon obtained career track labels. 2018-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4259 https://ink.library.smu.edu.sg/context/sis_research/article/5262/viewcontent/dshcm_2018_paper_6_career_tracks.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Label Propagation Career Movements Analysis Career Track Labelling Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Label Propagation
Career Movements Analysis
Career Track Labelling
Databases and Information Systems
spellingShingle Label Propagation
Career Movements Analysis
Career Track Labelling
Databases and Information Systems
CHIANG, Meng-Fen
LIM, Ee-peng
LEE, Wang-Chien
TIAN, Yuan
HUNG, Chih-Chieh
Are you on the right track? Learning career tracks for job movement analysis
description Career track represents a vertical career pathway, where one can gradually move up to take up higher job appointments when relevant skills are acquired. Understanding the propensity of career movements in an evolving job market can enable timely career guidance to job seekers and working professionals. To this end, we harvest career trajectories from online professional network (OPN). Our focus lies on obtaining a macro view on career movements at the track granularity. Specifically, we propose a semi-supervised career track labelling framework to automatically assign career tracks for large set of jobs. To contextually label jobs, we collect example jobs with career track labels identified by human resource specialists and domain experts in Singapore. An intuitive idea is to learn the labelling knowledge from the example jobs and then apply to jobs in OPN. Unfortunately, such small amount of labeled jobs presents a great challenge in our attempt to accurately recover career tracks for plentiful unlabelled jobs. We thus address the issue by resorting to semi-supervised learning methods. This research not only reduces the human annotation efforts in maintaining the career track knowledge databases over time across different geographical regions, but also facilitates data science study on career movements. Extensive experiments are conducted to demonstrate the labelling accuracy as well as to gain insights upon obtained career track labels.
format text
author CHIANG, Meng-Fen
LIM, Ee-peng
LEE, Wang-Chien
TIAN, Yuan
HUNG, Chih-Chieh
author_facet CHIANG, Meng-Fen
LIM, Ee-peng
LEE, Wang-Chien
TIAN, Yuan
HUNG, Chih-Chieh
author_sort CHIANG, Meng-Fen
title Are you on the right track? Learning career tracks for job movement analysis
title_short Are you on the right track? Learning career tracks for job movement analysis
title_full Are you on the right track? Learning career tracks for job movement analysis
title_fullStr Are you on the right track? Learning career tracks for job movement analysis
title_full_unstemmed Are you on the right track? Learning career tracks for job movement analysis
title_sort are you on the right track? learning career tracks for job movement analysis
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4259
https://ink.library.smu.edu.sg/context/sis_research/article/5262/viewcontent/dshcm_2018_paper_6_career_tracks.pdf
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