TALENT MATCH AI POWERED RESUME MATCHING PLATFORM PROJECT REPORT
Description
In the evolving landscape of talent acquisition, organizations face increasing challenges in efficiently identifying the right candidates from large pools of applicants. Traditional recruitment processes often rely on manual resume screening, which is not only time-consuming and resource-intensive but also prone to human bias and inconsistency. The TalentMatch project introduces an AI-powered recruitment platform designed to revolutionize this process by automating and optimizing resume-to-job matching using advanced Natural Language Processing (NLP) and Machine Learning (ML) techniques. The core objective of TalentMatch is to intelligently parse, analyze, and rank candidate resumes against job descriptions by extracting key information such as skills, qualifications, work experience, education, certifications, and contextual relevance to the role. The system utilizes semantic analysis to understand the meaning behind unstructured text data, enabling it to match candidates not just based on keyword overlap, but on true contextual alignment with job requirements. By converting unstructured resumes and job postings into structured data representations, the platform enables a fair, consistent, and data-driven approach to candidate evaluation. The platform architecture comprises a modular backend built with Express and TypeScript, which handles data ingestion, resume parsing, and job description analysis. The intelligent matching engine employs a combination of NLP techniques, such as named entity recognition (NER), word embeddings, vector similarity, and custom scoring algorithms to determine candidate-job fit. On the frontend, a responsive React application provides an intuitive interface for recruiters to view, filter, and interact with ranked candidate lists. The interface also supports customizable scoring metrics, enabling recruiters to prioritize specific attributes like technical skills, years of experience, or educational background based on the unique needs of a role. This report outlines the end-to-end development of TalentMatch, from the initial problem identification and design considerations to implementation, deployment, and performance evaluation. Special focus is given to the use of OpenAI's language models to enhance semantic understanding, as well as the integration of feedback mechanisms that allow for continuous improvement of the ranking system based on recruiter inputs. Extensive testing with real-world job descriptions and anonymized resumes demonstrates the system’s ability to significantly reduce screening time while maintaining or improving the quality of candidate selection. Additionally, the report explores ethical considerations, such as bias mitigation and data privacy, ensuring that the AI system supports fair hiring practices.
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Institutions
- Tribhuvan UniversityBagmati Province, Kathmandu