Matching Applicants with Positions for Better Allocation of Employees in the Job Market

Elgammal, Ziad; Barmu, Abdullah; Hassan, Hamza; Elgammal, Khaled Walid; Özyer, Tansel; Alhajj, Reda

doi:10.1109/acit53391.2021.9677374

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Z. Elgammal et al (2021) [25] made use of the SpaCy library to calculate the similarity between jobs and candidates and then rank candidates to arrive at an accuracy of 83%. The method described by Tejaswini et al (2021) [27] gives an accuracy of 92% using a smaller dataset and the accuracy significantly decreased as the number of candidates increased.…”

Section: Discussionmentioning

confidence: 99%

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Job-candidate classifying and ranking system with machine learning method

Boonchob

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Finding suitable candidates for an open job position could be a repetitive and time-consuming task, especially from a large pool of candidates. Besides, this task could truly make fair screening and shortlisting tedious. Losing the opportunity to hire top talent candidates due to the slow screening process or the wrong selection by human error is unacceptable. This paper presented a method for human resources to categorize and select the top�candidates for job opening they applied for. The proposed system directed to alter a machine learning algorithm to classify the candidate into groups i) shortlist and ii) not-suitable. The productive preprocessing data approaches of many works were applied. The Decision Tree, Support Vector Machine, Gaussian Naive Bayes, Random Forest, k-Nearest Neighbour, CatBoost, Extreme Gradient Boosting, and Convolution Neural Network were compared to find the most suitable classification model. Then, the system ranked the candidates in a shortlist group in descending order. The proposed system operates an accuracy of 83.5%, weighted f1-score of 86%, and recall of 79% from the Support Vector Machine classifier. This enables the business to identify suitable candidates for a certain position and make more informed decisions about who to invite for an interview.

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Section: Discussionmentioning

confidence: 99%

“…The last one is ranking and reporting, the results are used to put the candidates who are best qualified at the top and the candidates who are least qualified at the bottom. System accuracy was 83 percent overall [25].…”

Section: Matching Applicants With Positions For Better Allocation Of ...mentioning

confidence: 97%