Resume Ranking


EOI: 10.11242/viva-tech.01.05.089

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Citation

Maitri Bhagat, Riddhima Chinchane, Shweta Jha, "Resume Ranking", VIVA-IJRI Volume 1, Issue 5, Article 89, pp. 1-9, 2022. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

Resumes contain required content that aids in decision making during an organization's selection and ranking processes. Project managers and human resource personnel are frequently tasked with selecting the "right person for the right job" from hundreds or even thousands of candidate resumes. Incorrect recruitment or task assignment decisions can cost a company a lot of money. Because of the rapid growth of Internet-based recruiting, there are a large number of personal resumes in recruiting systems. So, in the existing method the job-seeker has to fill specific data about their resume in a manual form which takes a vast amount of time and then also the candidates are not satisfied by the job which the present system prefers according to their skills. The standard approach usually includes a labor-intensive procedure of manually penetrating through the appeal candidates, reviewing their resumes, and then producing a shortlist of suitable candidates to be interviewed. In this era of technology, handling a vast amount of resumes has become harder and more inaccessible at the same time. Whereas, the process of selecting a candidate based on their resume has not been entirely automated. As a result, in this work, we extract information using rule-based and statistical methods, and we use the LDA algorithm to achieve high accuracy in the ranking and parsing sections. The four main functions of this device are plain text extraction, preprocessing, segmentation, and information extraction. Although supervised and rule-based methods for extracting facts from resumes have been developed, they are heavily reliant on hierarchical structure information and massive volumes of labeled data, both of which are difficult to obtain in practice. According to experimental results on a real-world dataset, the method is both feasible and effective. Before being saved to the database, most proposed observations are analyzed using a set of Natural Language Processing (NLP) and pattern matching algorithms. This study proposes a model..Experimental results on a real-world dataset show that the algorithm is feasible and effective. Mostly all proposed observations are validated using a set of Natural Language Processing (NLP) and pattern matching techniques before being saved to the database. This research proposes a model which extracts valuable information from the resume and ranks it according to the preference and requirement of the described job extract.

Keywords

Data Extraction , Filtration , Naive resume matching, Ranking data , Score matching

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