Intelligent Recruitment System Using NLP

Sharma, Aastha; Singhal, Smiti; Ajudia, Dhara

doi:10.1109/aimv53313.2021.9670958

Cited by 6 publications

(2 citation statements)

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“…Particularly beneficial for recent graduates or those lacking resume expertise or time, this platform streamlines resume creation with its user-friendly interface. Mgarbi et al (2023) [16] designed an application centered on crafting visually appealing resumes using diverse templates and fonts. Users can also include links to their online profiles, such as LinkedIn and GitHub.…”

Section: Introductionmentioning

confidence: 99%

“…Zied Jaoua et al (2012) [3] proposed an algorithm for robust reception of compressed HTML files over noisy mobile radio channels, assuming standard compliance of both source encoders and transmission systems. Sharma et al (2021)[17] emphasized the increasing popularity of mobile browsing and the challenges posed by limited memory capacity and bandwidth for rendering HTML documents on mobile browsers. Ujjal Marjit et al (2012) [4] suggested utilizing linked data to recover CV information, developing an online Chinese resume parser that extracted data using a rule-based statistical algorithm.…”

Section: Introductionmentioning

confidence: 99%

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ResumeCraft: A Machine Learning-powered Web Platform for Resume Building

Shivhare

2024

IJRASET

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The competitive job market necessitates well-crafted resumes that resonate with both human recruiters and Applicant Tracking Systems (ATS). This paper introduces ResumeCraft, a web-based platform empowering users to build strong resumes and optimize them for ATS compatibility. ResumeCraft leverages Machine Learning (ML) for data analysis and user guidance, while the user interface is built with Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript for a user-friendly experience. The system allows users to input their personal and professional details through a series of form fields, and provides a real-time preview of the resume design as the user inputs their data. The resume generator uses JavaScript to dynamically populate the preview with the user's input, and allows users to select from a range of pre-designed templates and color schemes to customize the look and feel of their resume. It processes the user input and generates a downloadable Portable Document Format (PDF) of the final resume. The platform analyzes user-provided information through ML models, offering suggestions on skill extraction, keyword matching, and action verb usage. This combination empowers users to create impactful resumes that are more likely to pass through ATS filters and reach human reviewers.

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Section: Introductionmentioning

confidence: 99%