DSHCM 2017

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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: http://www.dshcm.org

Collocated with IEEE International Conference on Data Mining ICDM’17 http://icdm2017.bigke.org/

Description:

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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:

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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)

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========================================= SUBMISSION DEADLINE: August 7th, 2017

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Submission guidelines:

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==== 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.

Submissions have to conform to the IEEE ICDM'17 triple-blind submission guidelines. Please see the workshop website for submission instructions: www.dshcm.org

Special Journal Issue:

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=== The DSHCM chairs have finalized an agreement with the Editors-in-Chief of SpringerOpen Data Science and Engineering journal (http://www.springer.com/computer/database+management+%26+information+retrieval/journal/41019) 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.

Proceedings:

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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:

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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:

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== 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 Eric Lawrence, Indeed Mohammed Korayem, CareerBuilder

Workshop organizers:

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== Faizan Javed CareerBuilder Georgia, USA faizan.javed@careerbuilder.com

Ioana E. Marinescu Harris School of Public Policy, University of Chicago Illinois, USA ioanamarinescu@gmail.com

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