Enhancing recruitment efficiency: An advanced Applicant Tracking System (ATS)

Abstract

The Applicant Tracking System (ATS), also known as a talent management system or job applicant tracking system, is a software application designed to facilitate more efficient recruitment processes for companies or selection agencies. The objective of ATS is to streamline various aspects of the recruiting process, from receiving applications to hiring employees and effectively manage recruitment needs electronically. Methodologies such as NLP and KNN models are used for automated resume parsing and classifying the resume from unstructured format to structured format. The final results found significant improvement in performance of functionalities such as candidate screening, applicant testing, interview scheduling, managing the hiring process, reference checks, and completing new-hire paperwork.

Full text

Industrial Management Advances (2024) Volume 2 Issue 1
doi: 10.59429/ima.v2i1.6373
1
Research Article
Enhancing recruitment efficiency: An advanced Applicant Tracking
System (ATS)
Prasad R. Chavan, Yash Chandurkar, Ankita Tidake*, Gaurav Lavankar, Suhani Gaikwad, Rohit Chavan
Department of Artificial Intelligence and Data Science, Ajeenkya DY Patil School of Engineering, SPPU, India Pune,
India
* Corresponding author: Ankita Tidake, ankita.tidake@gmail.com
ABSTRACT
The Applicant Tracking System (ATS), also known as a talent management system or job applicant tracking system,
is a software application designed to facilitate more efficient recruitment processes for companies or selection agencies.
The objective of ATS is to streamline various aspects of the recruiting process, from receiving applications to hiring
employees and effectively manage recruitment needs electronically. Methodologies such as NLP and KNN models are
used for automated resume parsing and classifying the resume from unstructured format to structured format. The final
results found significant improvement in performance of functionalities such as candidate screening, applicant testing,
interview scheduling, managing the hiring process, reference checks, and completing new-hire paperwork.
Keywords: applicant tracking system, machine learning, natural language processing, KNN algorithm, job board, hiring
lifecycles
1. Introduction
An Applicant Tracking System (ATS) is a software solution employed by businesses to enhance and
automate their recruitment workflows. ATS platforms function as a centralized hub for HR departments,
allowing them to post job openings, monitor applicant data, and streamline the hiring process. They act as
repositories for storing and organizing candidate information, including resumes, cover letters, and application
materials. ATS systems are equipped with various features such as resume parsing, keyword search, and
automated email responses, enabling efficient handling of large volumes of applications. Recruiters can
leverage ATS systems to create job listings, monitor applicant statuses, and collaborate with hiring managers
seamlessly. The primary benefits of ATS systems include time and resource savings through automation of
repetitive tasks like resume screening and application tracking. Moreover, these systems aid in ensuring
compliance with hiring regulations by documenting and securely storing applicant data. ATS platforms
frequently include reporting and analytics features, empowering recruiters to monitor essential metrics like
time-to-fill and recruitment source. Additionally, they seamlessly integrate with other HR software elements
like payroll and onboarding systems, optimizing both the recruitment and onboarding procedures.
ARTICLE INFO
Received: 5 June 2024 | Accepted: 3 July 2024 | Available online: 8 July 2024
CITATION
Chavan PR., Chandurkar Y, Tidake A. Enhancing recruitment efficiency: An advanced Applicant Tracking System (ATS). Industrial Management
Advances 2024; 2(1): 6373. doi: 10.59429/ima.v2i1.6373
COPYRIGHT
Copyright © 2024 by author(s). Industrial Management Advances is published by Arts and Science Press Pte. Ltd. This is an Open Access article
distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), permitting distribution
and reproduction in any medium, provided the original work is cited.

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From a candidate perspective, ATS systems contribute to a positive experience by providing user-friendly
interfaces for submitting applications and monitoring progress. Recruiters can establish personalized
workflows and automate communication with candidates throughout the hiring journey. Furthermore, ATS
systems enhance talent acquisition efforts by leveraging social media and job board integrations to access a
broader pool of candidates. They facilitate interview scheduling, assessments, and management of candidate
feedback. ATS systems are available in both cloud-based and on-premises deployment options, catering to the
specific needs and preferences of organizations. Overall, ATS systems are indispensable tools in modern
recruitment practices, enabling companies to efficiently manage the entire hiring lifecycle with enhanced
productivity.
2. Problem statement
Amid the dynamic realm of contemporary recruitment, companies face a plethora of challenges, from
managing high volumes of applications to navigating compliance requirements and fostering candidate
engagement. Enter Applicant Tracking Systems (ATS), offering to transform recruitment processes and ease
the burdens burdening HR departments globally. The sheer influx of job applications, propelled by the ubiquity
of online platforms, inundates traditional recruitment practices, rendering manual processing inefficient and
prone to oversight. Standardization remains elusive, resulting in disparate evaluation methodologies and
disjointed workflows, further exacerbating the strain on resources and impeding timely candidate selection.
Moreover, the labyrinth of legal regulations governing hiring practices looms large, demanding meticulous
attention to detail to ensure compliance and mitigate the risk of legal repercussions. Amidst this complexity,
the imperative to cultivate a positive candidate Amidst the flood of applications, experience becomes a crucial
factor, yet attaining it faces significant hurdles. ATS systems provide a ray of hope, employing automation,
AI, and data analytics to streamline recruitment, speed up candidate screening, and improve talent acquisition
efficiency. However, fully realizing ATS potential is challenging. Integrating ATS into existing infrastructures
presents major obstacles, including compatibility issues and data transfer concerns. User adoption is a critical
bottleneck, with resistance to change and inadequate training impeding effective use of ATS features and
hindering productivity gains. Moreover, cost considerations loom large, particularly for resource-constrained
entities, with the initial investment in ATS implementation, ongoing maintenance, and subscription fees for
cloud-based solutions posing significant financial burdens. Yet, amidst these challenges lies an opportunity for
strategic transformation. By aligning ATS adoption with organizational objectives, investing in comprehensive
training programs, and fostering a culture of innovation, organizations can unlock the true potential of ATS
systems, paving the way for enhanced recruitment outcomes, improved candidate experiences, and sustained
competitive advantage in the talent acquisition landscape. Thus, while the road ahead may be fraught with
obstacles, the promise of ATS systems as catalysts for change remains undeniably compelling, offering a
beacon of hope in the quest for the recruitment excellence of the system.
3. Literature review
Paper 1: In their work titled “Automated Candidate Segregation and Personality Evaluation for
Recruitment Using Legation Regression,” G. Sudha, Sasipriya K K, Sri Janani S, Nivethitha D, Saranya S, and
Karthick Thyagesh G propose a novel solution to the perennial challenges faced by organizations in manual
candidate screening and personality assessment during the recruitment process. This innovative project
introduces an automated system designed to streamline recruitment procedures by facilitating the registration
of candidate details, conducting personality evaluations through online quizzes, and assessing professional
eligibility via analysis of uploaded CVs utilizing Logistic Regression techniques. Through the seamless
integration of these components, the system adeptly automates candidate segregation, personality assessment,

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and professional eligibility evaluation, thereby markedly enhancing the efficiency of the recruitment process
using machine learning algorithms and data analytics, the research critically assesses different approaches to
analyzing resume data, with the goal of enhancing and streamlining the candidate selection process. Moreover,
the research not only identifies areas for improvement within existing recruitment methodologies but also
delineates avenues for future exploration and advancement in the realm of resume analysis. This study
illuminates the transformative potential of automated systems in reshaping recruitment methodologies. It sets
the stage for embracing advanced technologies to pinpoint candidates with the necessary skills and attributes
for specific job roles, marking the dawn of a new era characterized by enhanced efficiency and effectiveness
in talent acquisition efforts.
Paper 2: In their work titled “Job Applications Selection and Identification: Study of Resumes with
Natural Language Processing and Machine Learning,” Amit Pimpalkar, Aastha Lalwani, Roshan Chaudhari,
Mohd. Inshall, Mahak Dalwani, and Tarandeep Saluja address the pervasive challenge confronting
organizations: efficiently navigating a deluge of job applications to pinpoint the most suitable candidates.
Manual categorization of resumes proves laborious and resource-intensive, prompting the need for innovative
solutions. This study aims to innovate the resume selection process by leveraging Natural Language Processing
(NLP) and Machine Learning (ML) techniques to deconstruct and evaluate the unstructured textual data found
within resumes. By deploying advanced algorithms, the objective is to expedite the identification of individuals
possessing the requisite skills and attributes sought by employers. In this comprehensive exploration, the study
delves into a diverse array of machine learning algorithms and data analysis methodologies, evaluating their
efficacy in enhancing resume analysis. Through rigorous experimentation and critical evaluation, the research
not only illuminates the strengths and weaknesses of existing approaches but also identifies untapped potential
and future avenues for advancement in the domain of resume analysis. By embarking on this quest for
innovation, the authors pave the way for transformative shifts in recruitment practices, offering a glimpse into
a future where the fusion of NLP and ML technologies facilitates seamless.
Paper 3: According to the paper [3] in simple terms, this study is about using technology to make the
process of selecting resumes for job positions easier. Companies often receive a lot of resumes and it can be
difficult and time-consuming to go through all of them to find the best candidates. The traditional way of
manually reading and categorizing resumes is not efficient. This study proposes the utilization of both Natural
Language Processing (NLP), enabling computers to comprehend human language, and Machine Learning
(ML), facilitating computers to learn from data, to expedite the analysis of resumes.
Paper 4: The author of the paper discussed a problem Utilizing Machine Learning and Text Mining for
Job Fair Optimization: A Big Data Approach. Yi-Chi Chou: Han-Yen Yu he studies addresses the need for
efficient job matching at large job fairs, in an environment with extensive participation from job applicants
and companies, necessitating tailored recommendations, this research endeavors to create an AI-driven system.
Leveraging machine learning, text mining, and big data technology, the system aims to analyze online
discussions, assess the personal competitiveness and personality traits of job applicants, and suggest job
vacancies based on electronic resumes. The experimental results confirm that the developed system
successfully provided job vacancy recommendations that aligned with the expectations of job applicants,
enhancing the efficiency of job fairs and job matching.
Paper 5: The authors delve into the intricacies of “Enhancing Personality Prediction from Resumes Using
Novel Random Forest and XG-Boost Algorithms,” authored by K. Maheswar Reddy and R. T. This scholarly
investigation tackles the formidable challenge of forecasting personality traits derived from resumes, aspiring
to elevate the precision of such predictions through the application of advanced machine learning algorithms.
The research unfolds by scrutinizing the efficacy of two prominent algorithms, namely the Novel Random

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Forest and XG-Boost, in discerning personality attributes from a curated corpus of 80 resume samples. The
findings of this empirical inquiry unveil compelling insights: the Novel Random Forest algorithm demonstrates
a remarkable accuracy rate of 90%, while its counterpart, XGBoost, exhibits a slightly lower yet commendable
accuracy of 86%. Furthermore, the study delves into the realm of hypothesis testing to rigorously assess the
statistical significance of the observed disparity in predictive accuracies between the two algorithms. This
rigorous analysis contributes not only to the burgeoning field of computational personality prediction but also
sheds light on the nuanced interplay between algorithmic methodologies and predictive outcomes. Through
meticulous experimentation and meticulous data analysis, the authors illuminate pathways toward more robust
and reliable personality prediction frameworks, thus enriching our understanding of the intricate nexus
between machine learning algorithms and the multifaceted dimensions of human personality as encapsulated
within the textual fabric of resumes.
4. System architecture
Designing the System Architecture for the Applicant Tracking System (ATS) involves structuring a
comprehensive framework that efficiently manages the entire recruitment lifecycle. At its core, the ATS
operates as a centralized software application, facilitating seamless communication and collaboration among
various stakeholders involved in the recruitment process. The architecture comprises several key components,
each playing a crucial role in enabling the system to automate tasks, streamline workflows, and enhance
decision-making. The front-end interface serves as the user-accessible gateway, providing intuitive and user-
friendly interfaces for recruiters, hiring managers, and candidates to interact with the system. It encompasses
features such as job posting forms, application submission portals, and candidate profile management tools,
designed to facilitate easy access to pertinent information and streamline user interactions. On the backend, a
robust database management system (DBMS) forms the foundation of the ATS, storing and organizing vast
amounts of applicant data, job listings, and recruitment-related information in a structured and easily
retrievable manner. The database architecture incorporates relational database models optimized for scalability,
performance, and data integrity, ensuring efficient storage and retrieval of information. Additionally, the
system incorporates advanced search and retrieval functionalities, allowing users to query the database based
on various criteria such as skills, experience, and qualifications to identify suitable candidates for specific job
roles.
Furthermore, the architecture incorporates sophisticated algorithms and data processing mechanisms to
automate various aspects of the recruitment process, enhancing efficiency and reducing manual intervention.
For instance, candidate screening algorithms utilize natural language processing (NLP) techniques to parse
and analyse resumes, extracting relevant information such as skills, experience, and education to assess
candidate suitability. Machine learning algorithms are utilized to examine past hiring data, detecting patterns
to enable predictive analytics. This aids in forecasting candidate success and refining recruitment strategies for
optimization. Additionally, the system integrates with external job boards, social media platforms, and career
websites through application programming interfaces (APIs) to facilitate broad-based recruitment efforts and
expand the candidate pool. Moreover, the architecture includes modules for interview scheduling, automated
email notifications, and collaboration tools to streamline communication and coordination among recruiters,
hiring managers, and candidates throughout the hiring process.

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Figure 1. System architecture of ATS.
From a scalability and deployment perspective, the ATS architecture is designed to accommodate varying
organizational needs and growth trajectories. It leverages cloud computing technologies to provide scalability
and flexibility, allowing organizations to scale resources dynamically based on demand and accommodate
fluctuations in recruitment volumes. Cloud-based deployment models offer advantages such as accessibility,
reliability, and cost-effectiveness, enabling organizations to deploy and manage the ATS with minimal
Furthermore, the infrastructure entails substantial overhead. Moreover, the architecture integrates robust
security protocols to protect sensitive applicant data and uphold compliance with data privacy regulations like
GDPR and HIPAA. Security features comprise encryption, access management, audit trails, and routine
security evaluations to minimize risks and prevent unauthorized access or data breaches.
In summary, the Applicant Tracking System's architecture is a comprehensive framework designed to
streamline recruitment processes, improve decision-making, and enhance talent acquisition efforts for
organizations and recruitment agencies alike. By leveraging advanced technologies such as cloud computing,
machine learning, and natural language processing, the system automates tasks, boosts efficiency, and provides
valuable insights to recruiters and hiring teams. With its scalable and secure design, the ATS provides a
dependable solution for overseeing the complete recruitment cycle, spanning from job advertisement to
candidate integration, all while adhering to regulatory standards and protecting the privacy of applicant data.
5. Methodology
The methodology employed in developing the Applicant Tracking System (ATS) project adopts a
systematic approach to tackle the intricate challenges inherent in contemporary recruitment processes. This
methodology consists of several meticulously planned phases, each geared towards ensuring the successful
conception, design, execution, and implementation of the ATS solution. The project initiation phase involves
extensive research and analysis to pinpoint crucial requirements, stakeholder demands, and industry standards.
This stage encompasses activities such as stakeholder interviews, examination of existing recruitment
procedures, and the establishment of clear project goals and success metrics. Subsequently, the requirements

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gathering phase begins, wherein various comprehensive techniques like interviews, surveys, and workshops
are utilized to capture detailed functional and non-functional requirements.
This stage emphasizes grasping user requirements, delineating system functionalities, and setting explicit
acceptance criteria to steer the development process efficiently. Following this, the system design phase
initiates, during which the architectural framework of the ATS solution is devised, drawing from the collated
requirements and stakeholder insights. This phase encompasses the design of system architecture, user
interfaces, database structure, and integration interfaces with external platforms like job boards and social
media channels. Prototyping and wireframing methodologies are employed to conceptualize system elements
and solicit input from stakeholders, guaranteeing conformity with user anticipations and usability benchmarks.
Following the completion of system design, the development phase commences, during which the ATS
solution is brought to life in accordance with the established architectural specifications and design principles.
Agile development methodologies like Scrum or Kanban may be embraced to support iterative progress,
allowing for the gradual rollout of features and swift adaptation to evolving requirements. The development
journey involves coding, testing, and rectifying software modules, while upholding coding standards and
employing rigorous testing methodologies to ensure the caliber and dependability of the software. Continuous
integration and continuous deployment (CI/CD) practices are harnessed to automate the build and deployment
procedures, hastening the delivery of new features and improvements.
Throughout the development phase, rigorous testing and quality assurance procedures are conducted to
validate the functionality, performance, and security of the ATS solution.
Figure 2. Block diagram of ATS.
This includes unit testing, integration testing, system testing, and user acceptance testing (UAT),
conducted in collaboration with stakeholders to ensure alignment with requirements and user expectations.
Test automation tools may be employed to streamline testing processes and ensure comprehensive test

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coverage, while performance testing tools assess system scalability, responsiveness, and reliability under
various load scenarios. Following successful testing and validation, the deployment phase begins. This
involves deploying the ATS solution to production environments according to deployment plans and release
schedules, configuring system settings, migrating data from legacy systems, providing end-user training, and
conducting post-deployment checks to ensure a smooth transition to the new system. Furthermore, change
management protocols are enacted to oversee stakeholder expectations, convey alterations to the system, and
handle any arising issues or inquiries during deployment. After deployment, the project transitions into the
maintenance and support phase, during which continuous maintenance tasks such as bug resolution,
performance refinement, and feature enhancements are executed to sustain the ongoing dependability and
efficacy of the ATS solution. User feedback mechanisms and monitoring tools are utilized to gather insights
into system utilization, pinpoint areas for enhancement, and promptly address concerns to bolster user
contentment and system efficiency, aligning with the overarching objectives of the system.
Throughout the project lifecycle, effective project management practices are employed to monitor
progress, manage risks, and ensure adherence to project timelines and budgets. This includes establishing
project governance structures, conducting regular status meetings, and fostering open communication channels
with stakeholders to facilitate collaboration and alignment towards project goals.
6. Result analysis
By following the above mentioned comprehensive methodology, the Applicant Tracking System project
is executed systematically, delivering a robust and effective solution that streamlines recruitment processes,
enhances decision-making, and drives organizational success. Implementation of ATS is done in stages and
following are the observations:
 Resume Shortlisting: Text Parsing and analysis resulted in extracting relevant information in structured
format from unstructured data. Information such as Skills and Experience showed significantly
improved shortlisting results.
 Job role-based Classification: k- nearest neighbour classified resumes based on skill-set, proficiency and
experience. Though it performed good, it became computationally cost inefficient, especially for large
datasets.
 Complex patterns and relationships were handled robustly as it made zero assumption about the
underlying data distribution
 ATS is able to perform on both Numerical and categorical data with precision making it flexible for
various applicant attributes
 Recruitment outcomes were in line with the organizational goals accomplishing the objective of our
project
7. Conclusion
In conclusion, the development and implementation of the Applicant Tracking System (ATS) project
represent a significant milestone in the quest to streamline recruitment processes, enhance decision-making,
and drive organizational success. By following a systematic approach that includes comprehensive research,
gathering requirements, designing, developing, testing, deploying, and maintaining, the project has resulted in
the development of a strong and efficient solution. This solution effectively tackles the intricate challenges
present in contemporary talent acquisition. The ATS solution serves as a testament to the power of technology

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to revolutionize traditional recruitment practices, offering organizations and selection agencies a sophisticated
toolset to navigate the intricacies of the recruitment lifecycle with efficiency and precision.
At its core, the ATS project is driven by a deep understanding of the evolving needs and expectations of
stakeholders, including recruiters, hiring managers, candidates, and organizational leadership. Through
extensive stakeholder engagement and requirements elicitation efforts, the project team has successfully
captured and translated user needs into tangible system functionalities, ensuring alignment with organizational
objectives and user expectations. The result is a feature rich ATS solution equipped with advanced capabilities
such as candidate screening, applicant testing, interview scheduling, and automated communication workflows,
empowering users to efficiently manage recruitment processes and identify top talent effectively.
Moreover, the ATS project embodies a commitment to excellence in software engineering practices,
leveraging industry-leading methodologies, tools, and technologies to deliver a high-quality solution that meets
stringent performance, reliability, and security standards. Agile development methodologies have facilitated
iterative development cycles, enabling rapid prototyping, feedback-driven refinement, and incremental
delivery of features to stakeholders. Continuous integration and deployment practices have streamlined
development workflows, ensuring seamless collaboration among team members and accelerating time-to-
market for new enhancements and updates. Comprehensive testing and quality assurance processes have
safeguarded the integrity and reliability of the ATS solution, mitigating risks and ensuring a superior user
experience for all stakeholders.
Moreover, the ATS project highlights the transformative power of data-driven decision-making in
recruitment procedures. By leveraging machine learning, natural language processing, and data analytics, the
project unlocks valuable insights from extensive pools of applicant data. Advanced algorithms facilitate
automated candidate screening, predictive analytics, and personalized recommendations, enabling recruiters
to make informed hiring decisions based on objective criteria and data-driven assessments. By leveraging
historical hiring data and identifying patterns, the ATS solution enables organizations to optimize recruitment
strategies, identify top-performing candidates, and foster diversity and inclusion in their talent pipelines. In
addition to its immediate impact on recruitment processes, the ATS project lays the foundation for ongoing
innovation and continuous improvement in talent acquisition practices. Post-deployment, the project team
remains committed to monitoring system performance, gathering user feedback, and iterating on the solution
to address emerging needs and evolving requirements. Continuous enhancement efforts focus on enhancing
user experience, expanding system capabilities.
Researchers can utilize this paper for building advanced application tracking system (ATS) by using
Knowledge-based Learning algorithms for semantic network analysis.
Conflicts of interest
The authors do not have any conflicts of interest.
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