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DSHCM 2018 : The 2nd International Workshop on Data Science for Human Capital Management (colocated with ECML-PKDD'18)


When Sep 14, 2018 - Sep 14, 2018
Where Dublin, Ireland
Submission Deadline Jul 2, 2018
Notification Due Jul 23, 2018
Final Version Due Aug 6, 2018
Categories    data science   human capital management   machine learning

Call For Papers

The 2nd International Data Science for Human Capital Management (DSHCM) workshop

Friday, 14th September 2018


Co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases


While most global economies have experienced steady job growth since the Great Recession in 2007-2008, many structural problems persist. Despite a record number of job openings, the issue of underemployment exists in many countries and has been attributed to the skills gap. The emergence of web and mobile job portals, online professional networks and training courses have further changed people’s behavior of job seeking, skill acquisition and professional development. At the same time, it’s paramount for companies to focus on talent management to ensure high levels of employee engagement and workforce performance. In a highly competitive job market, it’s also important to focus on reducing employee turnover which can have a detrimental effect on employee morale, project work and company expenses.

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. For stable labor markets and social welfare of communities, 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. Along with the socially conscious aspects of HCM, there is also a large market opportunity: the market size for HCM is estimated to be $131 billion. HCM is an industry which traditionally has not received much attention from experts in data science and machine learning. With the democratization of machine learning and artificial intelligence, there is great opportunity to bring awareness of HCM to more experts in the ICDM community and tackle problems which can have a wide social impact on a global scale.

There are many recent successful applications of data mining and data science techniques to problems in the HCM domain. For e.g., Text classification for job title 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; Graph mining for career pathing; Predictive analytics have been used to model employee flight risk and employee engagement; 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 guidelines:

This workshop welcomes submissions from both researchers and industry practitioners in HCM. Full paper submissions (maximum 16 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 8 pages) describing case studies or works-in-progress are also welcome.

Paper submissions should be a minimum of 4 pages long and should be submitted via EasyChair

Papers must be written in English and formatted according to the Springer LNCS guidelines. Author instructions, style files and copyright form can be downloaded here ( )

DSHCM chairs are currently in discussions with the Editors-in-Chief of SpringerOpen Data Science and Engineering journal to publish a special issue with a subset of high-quality accepted papers in DSHCM. Although this is an open access journal there is no charge for the authors of accepted papers to publish their work in this journal. More information about the special issue will be available soon.


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 ECML-PKDD’18 workshop proceedings published by Springer.

Important dates:

Workshop paper submission deadline: Monday, July 2, 2018

Workshop paper acceptance notification: Monday, July 23, 2018

Workshop paper camera-ready deadline: Monday, August 6, 2018

Workshop organizers:

Faizan Javed


Georgia, USA

Ee-Peng Lim

Singapore Management University


Mihai Rotaru

Textkernel BV

The Netherlands

Mohammad Al Hasan

Department of Computer Science

Indiana University - Purdue University

Indiana, USA

Program committee:

Qiaoling Liu, CareerBuilder

Yun Zhu, CareerBuilder

Panos Alexopoulos, Textkernel

Valentin Jijkoun, Textkernel

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

Moninder Singh, IBM TJ Watson Research Center

Maria Daltayanni, University of San Francisco

Daniel Kohlsdorf, Xing

Chen Zhu, Baidu HR

Parag Namjoshi, Workday

Wenjun Zhou, University of Tennessee

Vijay Dialani, LinkedIn

Manisha Verma, University College London

Pei-Chun Chen, Google

Nik Spirin, Datastars

Liangyue Li, Arizona State University

Lei Zhang, LinkedIn

Sandro Vega-Pons, Ultimate Software

Songtao Guo, LinkedIn

Min Xiao, ADP

Nick McClure, PayScale

Eric Lawrence, Indeed

Mohammed Korayem, CareerBuilder

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