Plataforma de pesquisa avançada: facilitando o acesso à informação a partir de filtros e machine learning

Abstract

This study presents the development of ProLeitura, an intelligent academic search system aimed at students and researchers from the Federal University of Rio Grande do Norte (UFRN). The platform seeks to optimize the process of finding relevant and reliable references by leveraging machine learning and modern tools such as Firebase and Google Custom Search API. The adopted methodology incorporated software engineering techniques, user-centered design (UCD), and machine learning. The system offers features like personalized search results, reference saving, and relevance prediction, providing an efficient and adaptive user experience. Results indicate significant improvements in search efficiency and reference quality, highlighting the positive impact of technology in academic environments. Finally, the study discusses identified limitations and proposes future enhancements to expand the system's capabilities.