Working paper Open Access

Identifying AI talents among LinkedIn members, A machine learning approach

Thomas Roca


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3240963</identifier>
  <creators>
    <creator>
      <creatorName>Thomas Roca</creatorName>
      <affiliation>Microsoft, Linkedin</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Identifying AI talents among LinkedIn members, A machine learning approach</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Artificial Intelligence</subject>
    <subject>Skills</subject>
    <subject>Machine learning</subject>
    <subject>Natural Language Processing</subject>
    <subject>Big Data</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-04-23</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Working paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3240963</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2649207</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/dfp17</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;How to identify specific profiles among the&amp;nbsp;hundred of millions gathered in LinkedIn?&amp;nbsp;LinkedIn Economic Graph thrives on skills,&lt;br&gt;
around 50 thousand of them are listed by&amp;nbsp;LinkedIn and constitute one of the main signals&amp;nbsp;to identify professions or trends. Artificial&amp;nbsp;Intelligence (AI) skills, for example, can be&amp;nbsp;used to identify the diffusion of AI in industries&amp;nbsp;[16]. But the noise can be loud around&amp;nbsp;skills for which the demand is high. Some&amp;nbsp;users may add &amp;quot;trendy&amp;quot; skills on their profiles&amp;nbsp;without having work experience or training&amp;nbsp;related to them. On the other hand, some&amp;nbsp;people may work in the broad AI ecosystem&amp;nbsp;(e.g. AI recruiters, AI sales&amp;nbsp;representatives,&amp;nbsp;etc.), without being the AI practitioners we&amp;nbsp;are looking for. Searching for keywords in profiles&amp;#39;&amp;nbsp;sections can lead to mis-identification of&amp;nbsp;certain profiles, especially for those related to&amp;nbsp;a field rather than an occupation. This is the&lt;br&gt;
case for Artificial Intelligence.&amp;nbsp;In this paper, we propose a machine learning&amp;nbsp;approach to identify such profiles, and suggest&lt;br&gt;
to train a binary text-classifier using job offers&amp;nbsp;posted on the platform rather than actual profiles.&lt;br&gt;
We suggest this approach allows to avoid&amp;nbsp;manually labeling the training dataset, granted&amp;nbsp;the assumption that job profiles posted by recruiters&amp;nbsp;are more &amp;quot;ideal-typical&amp;quot; or simply provide&amp;nbsp;a more consistent triptych &amp;quot;job title, job&amp;nbsp;description, associated skills&amp;quot; than the ones&amp;nbsp;that can be found among member&amp;#39;s profiles.&lt;/p&gt;</description>
  </descriptions>
</resource>
190
127
views
downloads
All versions This version
Views 190134
Downloads 12774
Data volume 451.7 MB265.2 MB
Unique views 170124
Unique downloads 11668

Share

Cite as