Thiago Ribeiro Furtado
Federal Center for Technological Education Celso Suckow da Fonseca
Papers
1
Total Citations
3
H-Index
1
About
Thiago Ribeiro Furtado is a researcher whose work sits at the intersection of data science, cultural analytics, and media studies. His primary research focuses on developing computational methods for understanding and classifying cultural content, particularly within the anime and entertainment industries. Furtado’s most notable contribution, "Anime clustering for automatic classification and configuration of demographics" (2023), addresses a critical gap in how recommendation algorithms handle niche cultural products. By applying clustering techniques to anime metadata, he demonstrates how automated classification can better capture demographic nuances, moving beyond simplistic genre tags to more sophisticated audience profiling. This work, which has garnered 3 citations, is significant for its practical implications: it offers a framework for content platforms to improve recommendation accuracy and for cultural producers to better understand their audiences. Furtado’s research is particularly timely, as the cultural industry increasingly relies on algorithmic systems to manage vast datasets and engage diverse viewer bases. His contributions highlight the potential of data-driven approaches to enhance both the consumption and production of media in an era of mass personalization.
Research Focus
Key Achievements
Top Papers
- 1