Uma Abordagem Híbrida para Sistemas de Recomendação com Base em Avaliações Textuais e Filtragem Colaborativa
DOI:
https://doi.org/10.5753/reic.2026.8474Keywords:
Sistemas de recomendação, Filtragem colaborativa, Avaliações textuais, Processamento de Linguagem NaturalAbstract
Sistemas de recomendação (SsR) utilizam cada vez mais avaliações textuais para aprimorar a modelagem de preferências. Embora os SsR baseados em revisões (RARs) capturem nuances semânticas, apresentam dificuldades em lidar com padrões colaborativos. A filtragem colaborativa (FC) tradicional é robusta em relações estruturais, mas limitada na semântica. Propomos uma abordagem híbrida que integra representações semânticas derivadas de RARs em modelos de FC. Avaliada em três conjuntos de dados da Amazon, nossa proposta demonstra que os modelos híbridos superam os modelos de referência, alcançando um erro de previsão até 2,8 vezes menor e uma precisão 60% maior, destacando seu potencial para recomendações robustas e contextualizadas.
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Copyright (c) 2026 Naan Vasconcelos, Yan Andrade, Adriano Pereira, Leonardo Rocha
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
