@article {10.3844/jcssp.2022.612.621,
article_type = {journal},
title = {A Recommendation-Based Contextual Model for Talent Acquisition },
author = {A., Channabasamma and Suresh, Yeresime},
volume = {18},
number = {7},
year = {2022},
month = {Jul},
pages = {612-621},
doi = {10.3844/jcssp.2022.612.621},
url = {https://thescipub.com/abstract/jcssp.2022.612.621},
abstract = {It is important to assist the job seekers in selecting the perfect jobs, which suit candidates' current skills and career objectives. Given the job description and resumes in the unstructured form, choosing the best job manually is a tedious task, so there is a need for an automated system to deal with raw data. The extraction of structured information from applicant resumes is needed not only to support the automatic screening of candidates but also to efficiently route candidates to the corresponding occupational categories based on their respective skills. The primary objective of this article is to process and extract relevant information from the unstructured data, like resumes in the form of .pdf, .doc, etc., using natural language processing. This study also proposes machine learning algorithms that exploit user context information to shortlist for the desired job role and also recommends alternative jobs to the candidates. Based on existing skills, new opportunities and possibilities will be introduced, which the candidate wouldn't have explored before. In addition, it also focuses on formalizing the problem of identifying the additional skills, taking into account the employee's existing skills. To exhibit the effectiveness of the proposed algorithms, various resumes have been passed and tried for different formats. The results obtained by the proposed method excel the traditional methods mathematically and practically.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}