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dc.contributor.advisorDeb Roy.en_US
dc.contributor.authorWang, Xiaomin,M. Eng.Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2020-09-15T22:02:37Z
dc.date.available2020-09-15T22:02:37Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/127537
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 47-49).en_US
dc.description.abstractThere are plenty job portals (e.g., linkedin.com, indeed.com, ziprecruiter.com etc), that leverage machine learning models to connect employers and job seekers via job or candidate recommendations. However, much less attention is paid to recommending specific skills that would help workers reskill or employers identify how to retrain their employees. This thesis seeks to build a system that recommends skills with the following two properties: 1) recommended skills are similar to a worker's existing skills so they are more likely to try and acquire them; 2) recommended skills increase chances of income enhancement. Existing research has largely focused on building models with employee data such as resumes and LinkedIn profiles. We instead explore the value of much-less-used employer data, i.e. language contained in job postings. The last few years have seen tremendous advances in natural language processing (NLP), including the rise of dense vector representations for text (i.e. "text embeddings") to help solve a plethora of prediction, classification, and other tasks. In building our system, we compare the performance of several language embedding models and skill valuation models to identify and recommend opportunities for re-skilling.en_US
dc.description.statementofresponsibilityby Xiaomin Wang.en_US
dc.format.extent49 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleExploring automated methods for supporting worker re-skillingen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1193031269en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2020-09-15T22:02:36Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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