CVR: An Automated CV Recommender System using Machine Learning Techniques

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creativework.keywordsCV,job
dc.contributor.advisorSifat Momen
dc.contributor.authorJannatul Ferdaou
dc.contributor.authorKanij Tamema Jahan Tama
dc.contributor.authorMd. Mahir Absar Bin Mohsin
dc.contributor.authorS. M. Shahriar Ferdous Shovon
dc.contributor.id1731733042
dc.contributor.id1811502042
dc.contributor.id1812777042
dc.contributor.id1812758042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2024-05-20
dc.date.accessioned2024-05-20T08:07:22Z
dc.date.available2024-05-20T08:07:22Z
dc.date.issued2022
dc.description.abstractIn the recruitment process, hand-picking the right candidate out of a pool of resumes in the allotted time can be quite daunting. Besides, there is always a risk of overlooking important information as there exists no specific format for writing CVs. This project focuses on extracting important information from CVs and job descriptions of various formats, using several machine learning techniques. Following this, the content-based recommendation technique is used to recommend candidates based on skillset. Currently, our work is limited to the computer science domain, with the expectation of expanding this to other fields in the future.
dc.description.degreeUndergraduate
dc.identifier.cd600000579
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/785
dc.language.isoen
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleCVR: An Automated CV Recommender System using Machine Learning Techniques
dc.typeProject
oaire.citation.endPage38
oaire.citation.startPage1
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