Authors :
Aachal Shah; Omkar Yadav; Shagun Verma; Sweta Nigam
Volume/Issue :
Volume 11 - 2026, Issue 4 - April
Google Scholar :
https://tinyurl.com/3vxcpjw8
DOI :
https://doi.org/10.38124/ijisrt/26apr771
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
The modern job market faces challenges such as a growing skill gap, unreliable resume evaluations, and employer fraud on online hiring platforms. Traditional recruitment methods do not effectively measure real technical abilities, while job seekers often find it difficult to identify legitimate opportunities. This paper presents JobOrbit, a real-time, cloud-driven recruitment system built using React TypeScript and Firebase Firestore. The platform features skill assessments based on hackathons, company verification, map-based job discovery, and permanent application records to improve fairness and transparency in the hiring process. A qualitative analysis, backed by user interviews and prototype testing, shows strong user trust, better engagement, and increased recruiter efficiency. Future improvements will include adding professional upskilling courses to address long-term skill gaps.
References :
- Shalaby, W., AlAila, B., Korayem, M., Pournajaf, L., AlJadda, K., & Quinn, S. (2018). Help Me Find a Job: A graph-based approach for job recommendation at scale. arXiv preprint.
- Zhao, J., Wang, J., Sigdel, M., Zhang, B., Hoang, P., Liu, M., & Korayem, M. (2021). Embedding-based recommender system for job to candidate matching on scale. arXiv preprint.
- Mujtaba, D. F., & Mahapatra, N. R. (2024). Fairness in AI-driven recruitment: Challenges, metrics, methods, and future directions. arXiv preprint.
- Taneja, K., Vashishtha, J., & Ratnoo, S. (2025). Fraud-BERT: Transformer based context-aware online recruitment fraud detection. Discover Computing, 28, 9.
- Goud, T. N., & Reddy, N. R. (2025). A machine learning approach for detecting fraudulent job postings in online recruitment platforms. Journal of Sensors, IoT & Health Sciences, 3(3), 14–28.
- Ullah, Z., & Jamjoom, M. (2023). A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques. PeerJ Computer Science, 9, e1234.
- Vidros, S., Kolias, C., Kambourakis, G., & Akoglu, L. (2017). Automatic detection of online recruitment frauds: Characteristics, methods, and a public dataset. Future Internet, 9(1), 6.
- Cracking the AI recruitment code: Striving for transparency in finding the right person-job fit. (2025). Information & Management, 62, 104156.
- Resume2Vec: Transforming applicant tracking systems with intelligent resume embeddings for precise candidate matching. (2025). MDPI Electronics, 14(4), 794.
The modern job market faces challenges such as a growing skill gap, unreliable resume evaluations, and employer fraud on online hiring platforms. Traditional recruitment methods do not effectively measure real technical abilities, while job seekers often find it difficult to identify legitimate opportunities. This paper presents JobOrbit, a real-time, cloud-driven recruitment system built using React TypeScript and Firebase Firestore. The platform features skill assessments based on hackathons, company verification, map-based job discovery, and permanent application records to improve fairness and transparency in the hiring process. A qualitative analysis, backed by user interviews and prototype testing, shows strong user trust, better engagement, and increased recruiter efficiency. Future improvements will include adding professional upskilling courses to address long-term skill gaps.
