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Identifying AI talents in LinkedIn database, A machine learning approach

Thomas Roca


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        <foaf:name>Thomas Roca</foaf:name>
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            <foaf:name>Microsoft, Linkedin</foaf:name>
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    <dct:title>Identifying AI talents in LinkedIn database, A machine learning approach</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dcat:keyword>Artificial Intelligence</dcat:keyword>
    <dcat:keyword>Skills</dcat:keyword>
    <dcat:keyword>Machine learning</dcat:keyword>
    <dcat:keyword>Natural Language Processing</dcat:keyword>
    <dcat:keyword>Big Data</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-04-23</dct:issued>
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    <dct:description>&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;</dct:description>
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