First Call for Papers - The 1st International Workshop on Data Science for Human Capital Management (collocated with IEEE ICDM'17)
DSHCM 2017
- URL: http://www.dshcm.org
- Event Date: 2017-11-18 ~ 2017-11-18
- Submission Date: 2017-08-07
- Location: New Orleans, United States of America
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FIRST CALL FOR PAPERS
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The 1st International Workshop on Data Science for Human Capital Management (DSHCM)
Website: www.dshcm.org
Collocated with IEEE International Conference on Data Mining ICDM’17 http://icdm2017.bigke.org/
Description:
Robust job creation, a skilled population and engaged employees are important socioeconomic elements for the economic success and social welfare of communities. For stable labor markets, it is important to match employers with the right candidates, provide opportunities for reskilling of the labor force, and ensure that the (post-hire) workforce is engaged and productive. Human Capital Management (HCM) refers to the set of practices and systems that facilitate talent acquisition and management. It encompasses the areas of talent and labor market analytics, job advertising and distribution, professional social networks, candidate sourcing, tracking, onboarding, benefits administration and compliance.
There are many recent successful applications of data mining and data science techniques to problems in the HCM domain. For e.g., Text classification techniques are used for job posting classification; Sequence labeling and statistical modeling approaches find application in resume and job parsing; Near-deduplication algorithms in concert with big data pipelines power many job aggregators; Predictive analytics model employee flight risk; Ontology mining techniques help build knowledge graphs of human capital entities; Personalized search and semantic search help job seekers by understanding searcher intent and contextual meaning of terms in the recruitment domain; Recommender systems have been used for expertise search and job recommendations.
Topics of interest
We solicit research works that are broadly related to data science on employment data, including data cleaning, data normalization, classification, clustering, and ranking. Specific topics of interest include (but are not limited to):
Machine learning for resume and job parsing
Data standardization, classification and normalization for Human Capital Management
Ontology mining for human capital knowledge graph construction
Large-scale information extraction and inference for HCM
Entity resolution and deduplication for HCM (e.g., people and job aggregators)
Reputation systems for worker rankings and expertise
Data mining for career pathing
Semantic job matching
Semantic search for recruitment
Recommender systems for e-recruiting
Labor market analytics for economic and workforce development (e.g., measuring skills gaps)
Labor market economics (e.g., impact of policy and regulation on hiring)
======================================================
SUBMISSION DEADLINE: August 7th, 2017
======================================================
Submission guidelines:
This workshop welcomes submissions from both researchers and industry practitioners in HCM. Full paper submissions (maximum 8 pages) are solicited in the form of research papers which propose new techniques and advances using data mining techniques for HCM, as well as industry papers that describe practical applications and system innovations in HCM application areas. Short papers (maximum 6 pages) describing case studies or works-in-progress are also welcome.
Please see the workshop website for submission instructions: www.dshcm.org
Proceedings:
At least one author of each accepted paper must complete the workshop registration and present the paper at the workshop, in order for the paper to be included in the proceedings. Accepted papers will be included in the IEEE ICDM 2017 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. The workshop proceedings will be in a CD separated from the CD of the main conference. The CD is produced by IEEE Conference Publishing Services (CPS).
Important dates:
August 7th, 2017: Submission deadline
September 7th, 2017: Notification of acceptance
September 18th, 2017: Camera-ready deadline for accepted papers
November 18th, 2017: Workshop date
Program committee:
Marios Kokkodis, Boston College
Qiaoling Liu, CareerBuilder
Yun Zhu, CareerBuilder
Panos Alexopoulos, Textkernel
Valentin Jijkoun, Textkernel
Vanessa (Wei) Feng, The Globe and Mail
Ioannis (Yanni) Antonellis, Upwork/Stanford
Emmanuel Malherbe, Multiposting
Simon Hughes, Dice
Andrew Pierce, ADP
Kush R. Varshney, IBM TJ Watson Research Center
K.N. Ramamurthy, IBM TJ Watson Research Center
Maria Daltayanni, University of San Francisco
Daniel Kohlsdorf, Xing
Miguel Pelaez-Fernandez, Georgia Tech
Chen Zhu, Baidu HR
Parag Namjoshi, Workday
Wenjun Zhou, University of Tennessee
Vijay Dialani, LinkedIn
Manisha Verma, University College London
Pei-Chun Chen, Google
Ye Tian, Google
Nik Spirin, Datastars
Liangyue Li, Arizona State University
Puneet Manchanda, University of Michigan - Ann Arbor
Haifeng Li, ADP
Lei Zhang, LinkedIn
Sandro Vega-Pons, Ultimate Software
Songtao Guo, LinkedIn
Min Xiao, ADP
Kulsoom Abdullah, ADP
Nick McClure, PayScale
Xuxu Wang, Workday
Elie Raad, Monster
Workshop organizers:
Faizan Javed
CareerBuilder
Georgia, USA
faizan.javed@careerbuilder.com
Ioana E. Marinescu
Harris School of Public Policy,
University of Chicago
Illinois, USA
ioanamarinescu@uchicago.edu
Mihai Rotaru
Textkernel BV
The Netherlands
rotaru@textkernel.nl
Mohammad Al Hasan
Department of Computer Science
Indiana University - Purdue University
Indiana, USA
alhasan@cs.iupui.edu
FIRST CALL FOR PAPERS
======================================================
The 1st International Workshop on Data Science for Human Capital Management (DSHCM)
Website: www.dshcm.org
Collocated with IEEE International Conference on Data Mining ICDM’17 http://icdm2017.bigke.org/
Description:
Robust job creation, a skilled population and engaged employees are important socioeconomic elements for the economic success and social welfare of communities. For stable labor markets, it is important to match employers with the right candidates, provide opportunities for reskilling of the labor force, and ensure that the (post-hire) workforce is engaged and productive. Human Capital Management (HCM) refers to the set of practices and systems that facilitate talent acquisition and management. It encompasses the areas of talent and labor market analytics, job advertising and distribution, professional social networks, candidate sourcing, tracking, onboarding, benefits administration and compliance.
There are many recent successful applications of data mining and data science techniques to problems in the HCM domain. For e.g., Text classification techniques are used for job posting classification; Sequence labeling and statistical modeling approaches find application in resume and job parsing; Near-deduplication algorithms in concert with big data pipelines power many job aggregators; Predictive analytics model employee flight risk; Ontology mining techniques help build knowledge graphs of human capital entities; Personalized search and semantic search help job seekers by understanding searcher intent and contextual meaning of terms in the recruitment domain; Recommender systems have been used for expertise search and job recommendations.
Topics of interest
We solicit research works that are broadly related to data science on employment data, including data cleaning, data normalization, classification, clustering, and ranking. Specific topics of interest include (but are not limited to):
Machine learning for resume and job parsing
Data standardization, classification and normalization for Human Capital Management
Ontology mining for human capital knowledge graph construction
Large-scale information extraction and inference for HCM
Entity resolution and deduplication for HCM (e.g., people and job aggregators)
Reputation systems for worker rankings and expertise
Data mining for career pathing
Semantic job matching
Semantic search for recruitment
Recommender systems for e-recruiting
Labor market analytics for economic and workforce development (e.g., measuring skills gaps)
Labor market economics (e.g., impact of policy and regulation on hiring)
======================================================
SUBMISSION DEADLINE: August 7th, 2017
======================================================
Submission guidelines:
This workshop welcomes submissions from both researchers and industry practitioners in HCM. Full paper submissions (maximum 8 pages) are solicited in the form of research papers which propose new techniques and advances using data mining techniques for HCM, as well as industry papers that describe practical applications and system innovations in HCM application areas. Short papers (maximum 6 pages) describing case studies or works-in-progress are also welcome.
Please see the workshop website for submission instructions: www.dshcm.org
Proceedings:
At least one author of each accepted paper must complete the workshop registration and present the paper at the workshop, in order for the paper to be included in the proceedings. Accepted papers will be included in the IEEE ICDM 2017 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. The workshop proceedings will be in a CD separated from the CD of the main conference. The CD is produced by IEEE Conference Publishing Services (CPS).
Important dates:
August 7th, 2017: Submission deadline
September 7th, 2017: Notification of acceptance
September 18th, 2017: Camera-ready deadline for accepted papers
November 18th, 2017: Workshop date
Program committee:
Marios Kokkodis, Boston College
Qiaoling Liu, CareerBuilder
Yun Zhu, CareerBuilder
Panos Alexopoulos, Textkernel
Valentin Jijkoun, Textkernel
Vanessa (Wei) Feng, The Globe and Mail
Ioannis (Yanni) Antonellis, Upwork/Stanford
Emmanuel Malherbe, Multiposting
Simon Hughes, Dice
Andrew Pierce, ADP
Kush R. Varshney, IBM TJ Watson Research Center
K.N. Ramamurthy, IBM TJ Watson Research Center
Maria Daltayanni, University of San Francisco
Daniel Kohlsdorf, Xing
Miguel Pelaez-Fernandez, Georgia Tech
Chen Zhu, Baidu HR
Parag Namjoshi, Workday
Wenjun Zhou, University of Tennessee
Vijay Dialani, LinkedIn
Manisha Verma, University College London
Pei-Chun Chen, Google
Ye Tian, Google
Nik Spirin, Datastars
Liangyue Li, Arizona State University
Puneet Manchanda, University of Michigan - Ann Arbor
Haifeng Li, ADP
Lei Zhang, LinkedIn
Sandro Vega-Pons, Ultimate Software
Songtao Guo, LinkedIn
Min Xiao, ADP
Kulsoom Abdullah, ADP
Nick McClure, PayScale
Xuxu Wang, Workday
Elie Raad, Monster
Workshop organizers:
Faizan Javed
CareerBuilder
Georgia, USA
faizan.javed@careerbuilder.com
Ioana E. Marinescu
Harris School of Public Policy,
University of Chicago
Illinois, USA
ioanamarinescu@uchicago.edu
Mihai Rotaru
Textkernel BV
The Netherlands
rotaru@textkernel.nl
Mohammad Al Hasan
Department of Computer Science
Indiana University - Purdue University
Indiana, USA
alhasan@cs.iupui.edu
Journal Promotion
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