Uma Abordagem Híbrida para Sistemas de Recomendação com Base em Avaliações Textuais e Filtragem Colaborativa - Andrade, Yan; Vasconcelos, Naan; Tuler, Elisa; Pereira, Adriano; Rocha, Leonardo (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the Web, 2025)
Recommender systems are increasingly leveraging textual reviews to enhance user preference modeling. Although review-aware recommender systems (RARs) have shown advances in capturing semantic nuances, they often fall short in representing collaborative patterns among users and items. In contrast, traditional collaborative filtering methods, though limited in semantic modeling, are robust in exploiting structural relationships. In this study, we propose a hybrid approach that integrates semantic representations derived from RARs into traditional collaborative models. We evaluated this strategy across three Amazon datasets, comparing RARs, collaborative, and hybrid models in terms of preference modeling accuracy (MSE and MAE), and recommendation relevance (Hits, Precision, Recall). Our results showthat hybrid models outperform both baselines, achieving up to 2.8 times lower prediction error and 60% higher precision. These findings highlight the potential of hybrid models to deliver more robust and context-aware recommendations.