Paperman - um sistema de recomendação de artigos científicos.

Abstract

The search for references and related work in scientific research can be an exhausting process, consuming an average of 4 hours per week for researchers. The abundance of sources and repositories makes it even more challenging to verify the veracity and reliability of these works, hindering the selection of high-quality, relevant research and leading to the disposal of half of the collected samples, negatively impacting productivity. Considering this scenario, the objective of this study is to plan and develop a platform to facilitate the initial stages of research through recommendation systems, models based on the researcher's profile, and data post-processing. The proposed system, called Paperman, employs natural language processing and machine learning techniques to analyze researchers' publication history and generate personalized recommendations for scientific articles. The system architecture includes an API for data collection and processing, integrations with external services such as ORCID and DBLP, and a browser extension that presents recommendations intuitively. Experimental results demonstrate the system's effectiveness, with metrics such as MRR of 0.8 and nDCG@5 of 0.9407, indicating high relevance of generated recommendations. The study contributes to the field of educational recommendation systems, offering a practical solution to optimize the literature review process and discovery of related works in scientific research. The Paperman system addresses common challenges in academic research, such as information overload and the need for efficient discovery of relevant publications, by leveraging the researcher's own profile and publication history to provide tailored recommendations.