• Login
    • Library Home
    View Item 
    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, MSc (Computer Science and Engineering)
    • View Item
    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, MSc (Computer Science and Engineering)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A document vectorization approach to Resume Ranking System(RRS)

    Thumbnail
    View/Open
    19366001_CSE.pdf (1.158Mb)
    Date
    2022-08
    Publisher
    Brac University
    Author
    Nabi, Norun
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10361/17980
    Abstract
    Technology transformed the way how job seekers apply for a job and recruiter’s hunting for a precise pick . Now, paper version of resume already become an outdated version of job application method. Electronic resume replaces the old method thanks to its easier access to technology. When it comes to a particular job requirement, screening a rele vant resume among thousands is an exhaustive and time consuming recruitment process because the respective HR of an organization must have a proof read the entire resume set to select the right person in the right position, a key decision for any organization. Extracting the semantic meaning from resume is otherwise a daunting task. By making the selection process fast and accurate, organizations could save huge efforts and money. Using state-of-art-technology could be a way out. In the field of NLP, there are a range of tools to classify documents. Document vectorization technique is a huge popular one among tech-communities. Documents like resumes could be categorized and ranked by applying such techniques and tools. Therefore choosing a most suitable vectorization al gorithm is pivotal. It is aimed to build a custom trained model specialized in vocabulary of resume based on frequency based word2vec model such as TF-IDF. However, to compare between job descriptions and resumes, Cosine-Similarity is consid ered to be the primary algorithm to find matching resumes whereas k-nearest neighbor algorithm has been used to group the desired documents. But the limitation comes with using fixed vocabulary size. TOPSIS is the most popular among Multi Criteria Decision Making algorithms. Along with vector similarity score, Other parameters like years of experience, university rankings could be normalized to consider for final ranking score.
    Keywords
    TF-IDF; Word2Vec; Doc2Vec; Cosine Similarity; Resume Classification; Recommendation Systems
     
    Description
    This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (page 24).
    Department
    Department of Computer Science and Engineering, Brac University
    Collections
    • Thesis & Report, MSc (Computer Science and Engineering)

    Copyright © 2008-2019 Ayesha Abed Library, Brac University 
    Contact Us | Send Feedback
     

     

    Policy Guidelines

    • BracU Policy
    • Publisher Policy

    Browse

    All of BracU Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Copyright © 2008-2019 Ayesha Abed Library, Brac University 
    Contact Us | Send Feedback