Feature Selection for Job Matching Application using Profile Matching Model

Rodriguez, Leah G.; Chavez, Enrico P.

doi:10.1109/ccoms.2019.8821682

Cited by 28 publications

(9 citation statements)

References 8 publications

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“…For a Pangasinan recruitment company, Rodriguez and Chavez developed a job matching system in 2019 with an effort to speed up the Hiring process Where Resumés and profiles are matched with job requirements using clustering algorithms by the system in an effort to increase data processing efficiency and reliability [7].…”

Section: Literature Surveymentioning

confidence: 99%

Skill Sync Selector Using Machine Learning Techniques

2024

IJSREM

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Our study introduces an Automatic Resume Scanner Application that makes an advantage of advanced natural language processing (NLP). Together with performing thorough linguistic analysis and effectively standardizing resumes, it leverages state-of-the-art natural language processing (NLP) algorithms for sentiment analysis, context interpretation, and profile ranking. Using machine learning algorithms, it matches candidate qualifications with job demands through an intuitive interface that allows criteria to be customized. The application additionally manages bias detection and reduction in order to advance equity and inclusivity in the hiring process. When everything is said and done, it provides a fast, accurate, and neutral way to screen candidates at a preliminary stage, saving time and fostering a diversity of ability. Key Words: Resume screening, machine learning, Natural Language Processing(NLP), Talent acquisition,Text mining, KNeighbors Classifier, OnevsRest Classifier, TF-IDF vectorization, Label encoding, Feature engineering.

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Section: Literature Surveymentioning

confidence: 99%

Skill Sync Selector Using Machine Learning Techniques

2024

IJSREM

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“…Prokhorov and Safronov (2019) provided an outline of natural language processing and machine learning approaches, which are very much essential to modern information systems to search and filter text documents (Prokhorov and Safronov, 2019;Deepa and Rajeswara, 2017). Rodriguez and Chavez (2019) identified the required key attributes of profile in the process of job matching. This study implemented a prominent feature selection approach that extracts the most cogent attributes required for job matching.…”

Section: Literature Surveymentioning

confidence: 99%

A Recommendation-Based Contextual Model for Talent Acquisition

Channabasamma¹,

Suresh²

2022

Journal of Computer Science

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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, 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.

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“…Rodrigues and Chavez [1] in their paper described job matching to be a way to control exactly how a job register and a job candidate are correctly paired together. Their paper extracted the attributes from the resumes and then used WEKA for data mining, they then adopted clustering algorithm to match the profile of the job seekers against the job requirements given by the respective companies.…”

Section: Reviewmentioning

confidence: 99%

Untitled

2022

IJSCAI

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The job market has expanded exponentially in the past few years. With many recruiters and candidates, it is not an easy task to match a perfect candidate with a perfect job. The recruiter targets candidates with the required skill sets mentioned in the job descriptions, while candidates target their dream jobs. The search frictions and skills mismatch are persistent problems. In this paper, we build a model that would match companies with candidates with the right skills and workers with the right company. We have further developed an algorithm to investigate people's hiring history for better results.

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Feature Selection for Job Matching Application using Profile Matching Model

Cited by 28 publications

References 8 publications

Skill Sync Selector Using Machine Learning Techniques

Skill Sync Selector Using Machine Learning Techniques

A Recommendation-Based Contextual Model for Talent Acquisition

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