A document vectorization approach to Resume Ranking System(RRS)

A document vectorization approach to Resume Ranking System(RRS)

This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.

Detalhes bibliográficos
Autor principal: Nabi, Norun
Outros Autores: Hossain, Dr. Muhammad Iqbal
Formato: Tese
Idioma:English
Publicado em: Brac University 2023
Assuntos:
TF-IDF
Word2Vec
Doc2Vec
Cosine Similarity
Resume Classification
Recommendation Systems
Resumes (Employment) > Software.
Acesso em linha:http://hdl.handle.net/10361/17980
id 10361-17980
record_format dspace
spelling 10361-179802023-03-27T06:36:36Z A document vectorization approach to Resume Ranking System(RRS) Nabi, Norun Hossain, Dr. Muhammad Iqbal Department of Computer Science and Engineering, Brac University TF-IDF Word2Vec Doc2Vec Cosine Similarity Resume Classification Recommendation Systems Resumes (Employment)--Software. 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). 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 Now, paper version of resume already become an outdated version of job application method.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. Norun Nabi M. Computer Science and Engineering 2023-03-14T10:22:55Z 2023-03-14T10:22:55Z 2022 2022-08 Thesis ID: 19366001 http://hdl.handle.net/10361/17980 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 32 pages application/pdf Brac University
institution Brac University
collection Institutional Repository
language English
topic TF-IDF
Word2Vec
Doc2Vec
Cosine Similarity
Resume Classification
Recommendation Systems
Resumes (Employment)--Software.
spellingShingle TF-IDF
Word2Vec
Doc2Vec
Cosine Similarity
Resume Classification
Recommendation Systems
Resumes (Employment)--Software.
Nabi, Norun
A document vectorization approach to Resume Ranking System(RRS)
description This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.
author2 Hossain, Dr. Muhammad Iqbal
author_facet Hossain, Dr. Muhammad Iqbal
Nabi, Norun
format Thesis
author Nabi, Norun
author_sort Nabi, Norun
title A document vectorization approach to Resume Ranking System(RRS)
title_short A document vectorization approach to Resume Ranking System(RRS)
title_full A document vectorization approach to Resume Ranking System(RRS)
title_fullStr A document vectorization approach to Resume Ranking System(RRS)
title_full_unstemmed A document vectorization approach to Resume Ranking System(RRS)
title_sort document vectorization approach to resume ranking system(rrs)
publisher Brac University
publishDate 2023
url http://hdl.handle.net/10361/17980
work_keys_str_mv AT nabinorun adocumentvectorizationapproachtoresumerankingsystemrrs
AT nabinorun documentvectorizationapproachtoresumerankingsystemrrs
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