TCC Sistemas de Informação

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Random food: sistema de recomendação de restaurantes
(2025-05-07) Euzébio, Emylle Chrystinne da Silva; Kamei, Fernando Kenji; http://lattes.cnpq.br/5033020411757389; Medeiros, Flávio Mota; http://lattes.cnpq.br/1874496667181567; Kamei; Soares , Elvys Alves; http://lattes.cnpq.br/6415531537733982; Moraes Júnior, Edison Camilo de; http://lattes.cnpq.br/8234380347792761
This final course work presents the development of Random Food, a restaurant recommendation system that seeks to facilitate the choice of meals through controlled randomness. The proposal arose from the observation of a common pain point among users who are indecisive about where to eat, especially given the wide range of establishments and delivery apps. The system was built using React on the frontend, Node.js on the backend, Firebase/Firestore as a database, and solutions such as Vercel, Render and Railway for free cloud hosting. The project adopts versioning practices with Git and GitHub and was documented with a focus on functional and non-functional requirements and database structure. In addition, tests were performed to ensure the stability of the application. Random Food already has an official software registration with INPI, and this work seeks not only to describe its technical construction, but also to present its relevance as a creative, functional and low-cost solution.