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@article{200728,
        author = {P Ajay and K. Arjun and B. Nischay and Ms. Rama Lakshmi},
        title = {AI-Based Internship Recommendation Engine for PM Internship Scheme},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2947-2955},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200728},
        abstract = {Students who want assistance in finding proper internship positions face challenges when they attempt to use large internship platforms which lack personalized support to match their abilities with suitable job vacancies. The Internship Recommendation Engine functions as an artificial intelligence system which matches skills with specific internship requirements based on candidate preferences for field of study and geographic location and their level of experience. The system employs Sentence Transformer embeddings to comprehend the meaning of both user profiles and internship requirements instead of depending on keyword matching. The system uses FAISS indexing technology to store representations which enables users to conduct rapid and precise similarity searches across a complete database of actual internship job postings. The system uses a recommendation ranking formula which evaluates multiple factors including semantic similarity and skill fit and location preference and experience level to establish priority order. The general filters which users select activate the domain diversity mechanism to create new entries which stop the system from producing identical results. The resume upload process enables automatic skill extraction for technical skills through natural language processing technology. The system uses a large language model to identify skill gaps for each role recommendation and it develops structured learning pathways. The complete system operates as a web application which provides five top internship recommendations together with detailed information about required qualifications. The evaluation results demonstrate that this method outperforms basic keyword filtering by accurately matching candidates with suitable job positions.},
        keywords = {Internship Recommendation, Semantic Similarity, Sentence Transformers, FAISS, Skill Gap Analysis, Natural Language Processing.},
        month = {May},
        }