InternHub: A Resume-Driven Internship & Job Recommendation Framework with Integrated Scam Screening, Application Tracking and Carrer & Skill Gap Analytics

Students seeking internships face bloated repositories of postings, frequently plagued by cross-platform duplicates, expired vacancies, and fraudulent stipend schemes. Contemporary literature on internship recommendation primarily concentrates on ranking algorithms under the assumption that pre-structured candidate profiles already exist and that all listings are verified. This paper presents InternHub, an end-to-end web framework that addresses the entire internship discovery and management pipeline. A student uploads an unstructured PDF resume; a dual-condition negative heuristic filter intercepts non-resume documents (e.g., college tuition fee receipts and financial bills); and a natural language processing pipeline extracts a canonical taxonomy of technical skills, project experiences, and target role classifications. In parallel, internship postings are retrieved concurrently across five live job APIs (JobDataAPI, Jooble, Adzuna, Remotive Open API, and Arbeitnow), transformed to a unified schema, pruned of dead links via mirror synchronization, and de-duplicated using composite tuple keys. A rule-based Trust Score Engine screens postings for scam indicators, penalizing upfront fees, unverified chat channels, and truncated specifications. Surviving opportunities are scored and ranked via a mathematically bounded five-factor model (core skills 50%, role alignment 20%, preferred skills 15%, project implementation evidence 10%, work-mode/location preferences 5%), retaining TF-IDF cosine similarity as a lexical baseline. For each opportunity, InternHub conducts a skill-gap analysis, suggests targeted resume refinements, and generates a progressive four-phase learning roadmap. Application states are followed across a six-stage lifecycle (Saved, Applied, Interview, Selected, Rejected, Withdrawn). The platform is implemented using React 19, TypeScript, Express.js, a FastAPI NLP microservice (PyMuPDF, spaCy, scikit-learn), and Supabase PostgreSQL with RowLevel Security (RLS). Functional verification across an 18-scenario automated suite achieved a 100% pass rate, with full end-toend recommendation requests averaging 1.84 s.