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Talent recruitment analytics in the era of big data

Descriptive

TitleInfo
Title
Talent recruitment analytics in the era of big data
Name (type = personal)
NamePart (type = family)
Meng
NamePart (type = given)
Qingxin
NamePart (type = date)
1988
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Meng, Qingxin, 1988-
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RoleTerm (authority = RULIB)
author
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Xiong
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Hui
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Hui Xiong
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Advisory Committee
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chair
Name (type = personal)
NamePart (type = family)
Lin
NamePart (type = given)
Xiaodong
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Xiaodong Lin
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Advisory Committee
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internal member
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Papakonstantinou
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Periklis A
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Periklis A Papakonstantinou
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Advisory Committee
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internal member
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NamePart (type = family)
Xiao
NamePart (type = given)
Fang
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Fang Xiao
Affiliation
Advisory Committee
Role
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outside member
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NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - Newark
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school
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Text
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theses
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2020
DateOther (qualifier = exact); (type = degree)
2020-05
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English
Abstract (type = abstract)
This dissertation aims at developing effective and efficient data mining techniques to solve varied talent recruitment issues, reforming the overall process with respect to talent sourcing, screening, matching, and assessment. Intelligent talent recruitment has gained increasing attention due to the critical talent competitions and intensive talent mobilities over the years. Previous studies mainly focus on discovering conceptual and theoretical topics, while applications for supporting organizational decision making are still under-explored.

To this end, we propose several approaches purposed to not only help the people to make intelligent talent-related decisions but also obtain domain understandings through a multifaceted data-driven perspective. In particular, we first present a hierarchical career-path-aware neural network to study individuals’ job mobilities. In this work, two problems are predicted all together on the basis of one’s historical career paths: 1) who will the individual’s next employer? 2) How long will the individual stay with his/her next employer? Several job mobility patterns regarding working duration, firm types, and etc. are discovered simultaneously. Also, we propose an intelligent matrix factorization based framework to address job salary benchmarking tasks. In this work, we consider multiple contextual factors to improve the prediction accuracy, such as job responsibility, company features, work location, and the time the job wanted. Furthermore, we put forward a Non-parametric Dirichlet Process-based graphical model to address the “cold-start” problem for salary benchmarking, which also has superior interpretability associated with job responsibility and company.
Subject (authority = local)
Topic
Data mining
Subject (authority = RUETD)
Topic
Management
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
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ETD
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TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
Identifier (type = local)
rucore10002600001
Identifier
ETD_10828
Identifier (type = doi)
doi:10.7282/t3-fc62-yc63
PhysicalDescription
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application/pdf
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text/xml
Extent
1 online resource (xi, 133 pages)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Location
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NjNbRU
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Meng
GivenName
Qingxin
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-04-25 23:45:41
AssociatedEntity
Name
Qingxin Meng
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - Newark
AssociatedObject
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License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Type
Embargo
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-05-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2022-05-31
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 31st, 2022.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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