Train Track: A Personalized Internship Recommendation System

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dc.contributor.authorDana Samer Najjar
dc.contributor.authorRaneen Yousef Samara
dc.contributor.authorSadeen Ghaith Hmouda
dc.date.accessioned2025-06-22T10:08:49Z
dc.date.available2025-06-22T10:08:49Z
dc.date.issued2025-06-18
dc.description.abstractTrain Track is a personalized internship recommendation system developed to assist final-year university students in identifying training opportunities that align with their academic background, technical and non-technical skills, and personal preferences. In response to the challenges students face during the internship search process—such as lack of structure, overwhelming options, and misaligned placements—this system introduces a guided, intelligent alternative. Built on a three-tier architecture using Flask for the backend, Laravel Blade for the frontend, and MySQL for data storage, the system collects student input through a six-step wizard. This includes personal information, academic subjects of interest, technical competencies, non-technical strengths, and optional preferences related to company size, training mode, and work culture. A weighted recommendation engine processes these inputs to generate tailored internship suggestions, ranked by fit level (e.g., Perfect Match, Strong Match, and Fallback). A fallback mechanism ensures that all users receive at least one viable result, even in cases of limited alignment. Train Track not only enhances student confidence and decision-making, but also provides institutions with a scalable tool to support training coordination. By bridging the gap between academic preparation and industry expectations, the system empowers students to pursue meaningful internships that support their professional growth and long-term goals.
dc.identifier.urihttps://hdl.handle.net/20.500.11888/20128
dc.language.isoen_US
dc.supervisorDr.Ahmad Shraideh
dc.titleTrain Track: A Personalized Internship Recommendation System
dc.typeGraduation Project
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