SmartIntern: AI-Based Internship Recommendation Engine for PM Internship Scheme
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Abstract
The Internship Recommendation System is an intelligent platform leveraging machine learning and modern web technologies to enhance job discovery. By analyzing user Details and resumes through NLP, the system generates personalized recommendations. This study proposes the concept of an AI-Enabled Job Portal, an all-encompassing platform that aims to close the gap between qualified individuals and available opportunities through Intelligent Matching Algorithms and Natural Language Processing (NLP). Built using React.js and Node.js/Flask, with MongoDB and IPFS-like resume storage, it includes employer dashboards, secure authentication, and real-time tracking. This paper presents the system architecture, implementation details, and performance evaluation, showcasing its ability to transform recruitment processes with personalization and intelligent automation
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References
[1] J. R. Arun Kumar, B. Gupta, N. Kumar, D. Choudhary, and L. Jain, “Job and internship recommendation system,” JSCER, vol. 8, no. 5, pp. 1–10, 2025.
[2] M. Alsaif, A. Alshammari, and H. Alotaibi, “Learning-based job recommendation system,” Computers, vol. 11, no. 11, pp. 1–15, 2022.
[3] S. Rao, P. Sharma, and R. Mehta, “AI-based job recommendation system using skill extraction and matching,” IJRASET, vol. 14, no. 2, pp. 45–52, 2026.
[4] A. Kumar, R. Singh, and P. Verma, “AI-powered resume analyzer and job matching system,” IJRIAS, vol. 9, no. 3, pp. 12–20, 2025.
[5] R. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331–370, 2002.
[6] X. Zhou, Y. Xu, and J. Li, “Deep learning approaches for job recommendation systems,” Springer AI Research, vol. 5, no. 2, pp. 101–115, 2020.
[7] L. Chen, Q. Zhang, and Y. Jin, “Artificial intelligence in recommender systems,” Springer, vol. 3, no. 1, pp. 1–20, 2021.
[8] P. Resnick and H. Varian, “Recommender systems,” Communications of the ACM, vol. 40, no. 3, pp. 56–58, 1997.
[9] S. Tjandra, B. Hartono, and L. Nugroho, “KNN-based job recommendation system,” IJCRT, vol. 13, no. 6, pp. 1–7, 2025.
[10] F. Abdulrahman, M. Hassan, and T. Ali, “Machine learning-based employee recommendation system,” IJCA, vol. 14, no. 2, pp. 21–28, 2024.
[11] A. Dhoor, L. Singh, and P. Mehta, “Online job portal with recommendation system,” IJCRT, vol. 12, no. 6, pp. 1–9, 2023.
[12] L. S. Bhadouria, A. Tiwari, and R. Patel, “Online recruitment and recommendation system,” IJIRT, vol. 19, no. 4, pp. 45–52, 2023.