We use cookies to ensure the functionality of our website, to personalize content and advertising, to provide social media features, and to analyze our traffic. If you allow us to do so, we also inform our social media, advertising and analysis partners about your use of our website. You can decide for yourself which categories you want to deny or allow. Please note that based on your settings not all functionalities of the site are available.
Further information can be found in our privacy policy.
Recent Comments
Dear Editors,
I would like to submit the following chapter proposal for consideration in your edited volume, From Linear to Circular Business Models: AI-Driven Sustainable Growth in Emerging Markets.
Proposed Chapter Title
AI-Driven Talent Matching in Emerging Economies
Abstract
Placement cells in higher education institutions across emerging economies continue to rely on manual, subjective processes to evaluate student profiles and match them with job opportunities. This leads to inefficiencies, missed placements, and a lack of structured feedback for students. With the rapid growth of the job market and increasing volumes of applicants, there is a clear need for intelligent, data-driven systems that can bridge the gap between student capabilities and employer requirements.
Objectives
This study aims to: (i) design and develop a web-based placement dashboard integrating AI-assisted resume analysis, (ii) implement a rule-based job suitability indicator to classify candidates as strong, moderate, or weak matches, and (iii) evaluate the effectiveness of the recommendation logic using standard metrics including Precision, Recall, F1-Score, and Mean Reciprocal Rank (MRR).
Research Methodology
The system follows an agile development methodology organized into iterative sprints. The technical stack includes React.js and Next.js for the frontend, FastAPI for the backend, and SQLite for storage. The AI pipeline leverages Sentence BERT for semantic embeddings, spaCy for named entity recognition, FAISS for vector search, and cosine similarity for candidate-job matching. The scoring model weights skill match at 40%, project relevance at 25%, experience at 25%, and CGPA at 10%.
Major Findings
The developed system, ResumeRank, successfully integrates AI-generated outputs into a structured placement dashboard accessible to both students and placement officers. Students receive resume-quality scores, skill feedback, and job match rankings, while recruiters can filter, shortlist, and rank candidates efficiently. The rule-based suitability indicator demonstrates reliable classification across match categories. Key limitations include reliance on structured resume inputs and the absence of deep learning-based matching in the current prototype.
Conclusion
ResumeRank demonstrates that AI-driven talent matching tools can meaningfully improve placement efficiency in emerging market institutions. By serving both students and placement cells through a unified platform, the system establishes a scalable foundation for data-driven recruitment. Future work includes role-based access control, real-time analytics, and advanced deep learning matching models.
Keywords: AI talent matching, resume analysis, placement management, emerging economies, NLP, job suitability, campus recruitment
Authors Amitesh S T (1ds24cb007@dsce.edu.in),
Anant Sharma (1ds24cb008@dsce.edu.in),
Anirudh G Kharvi (1ds24cb011@dsce.edu.in),
Shashank Sharma (1ds24cb050@dsce.edu.in),
Prof. Anil D,
Dr. Archana Nandibewoor
authors affiliation
Amitesh S T, Anant Sharma, Anirudh G Kharvi, and Shashank Sharma are undergraduate students at Dayananda Sagar College of Engineering, Bengaluru, India. Anil. D Professor and Dr. Archana Nandibewoor HOD of CSBS Dept at Dayananda Sagar College of Engineering
Dear Editors,
I would like to submit the following chapter proposal for consideration in your edited volume, From Linear to Circular Business Models: AI-Driven Sustainable Growth in Emerging Markets.
Proposed Chapter Title
AI-Driven Talent Matching in Emerging Economies
Abstract
Placement cells in higher education institutions across emerging economies continue to rely on manual, subjective processes to evaluate student profiles and match them with job opportunities. This leads to inefficiencies, missed placements, and a lack of structured feedback for students. With the rapid growth of the job market and increasing volumes of applicants, there is a clear need for intelligent, data-driven systems that can bridge the gap between student capabilities and employer requirements.
Objectives
This study aims to: (i) design and develop a web-based placement dashboard integrating AI-assisted resume analysis, (ii) implement a rule-based job suitability indicator to classify candidates as strong, moderate, or weak matches, and (iii) evaluate the effectiveness of the recommendation logic using standard metrics including Precision, Recall, F1-Score, and Mean Reciprocal Rank (MRR).
Research Methodology
The system follows an agile development methodology organized into iterative sprints. The technical stack includes React.js and Next.js for the frontend, FastAPI for the backend, and SQLite for storage. The AI pipeline leverages Sentence BERT for semantic embeddings, spaCy for named entity recognition, FAISS for vector search, and cosine similarity for candidate-job matching. The scoring model weights skill match at 40%, project relevance at 25%, experience at 25%, and CGPA at 10%.
Major Findings
The developed system, ResumeRank, successfully integrates AI-generated outputs into a structured placement dashboard accessible to both students and placement officers. Students receive resume-quality scores, skill feedback, and job match rankings, while recruiters can filter, shortlist, and rank candidates efficiently. The rule-based suitability indicator demonstrates reliable classification across match categories. Key limitations include reliance on structured resume inputs and the absence of deep learning-based matching in the current prototype.
Conclusion
ResumeRank demonstrates that AI-driven talent matching tools can meaningfully improve placement efficiency in emerging market institutions. By serving both students and placement cells through a unified platform, the system establishes a scalable foundation for data-driven recruitment. Future work includes role-based access control, real-time analytics, and advanced deep learning matching models.
Keywords: AI talent matching, resume analysis, placement management, emerging economies, NLP, job suitability, campus recruitment
Authors Amitesh S T (1ds24cb007@dsce.edu.in),
Anant Sharma (1ds24cb008@dsce.edu.in),
Anirudh G Kharvi (1ds24cb011@dsce.edu.in),
Shashank Sharma (1ds24cb050@dsce.edu.in),
Prof. Anil D,
Dr. Archana Nandibewoor