Aprimorando a Personalização de Sistemas de Recomendação por Calibração Multicategoria - Atauchi, Paul; Zanon, André Levi; Rocha, Leonardo; Manzato, Marcelo G. (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.16145, 2025)
Recommender systems (RSs) are widely used to personalize suggestions based on users’ historical data. Despite recent advances, many RSs still prioritize accuracy metrics, often generating nonpersonalized lists that fail to reflect users’ actual interests. Calibration strategies have been proposed to address this misalignment by tailoring recommendations to users’ profiles. However, most approaches consider only a single category (e.g., genre), limiting their ability to capture the multifaceted nature of user preferences. This paper proposes a multi-category calibration method that simultaneously incorporates multiple categories extracted from knowledge graphs. The approach aims to balance recommendation relevance with alignment across diverse user profile dimensions. We evaluate the method on two well-known datasets, MovieLens and LastFM, using two contrasting recommendation algorithms: NCF (neural) and BPR-MF (non-neural). Results show that multicategory calibration can improve alignment with multifaceted user preferences while maintaining or enhancing accuracy, fostering fairer, more personalized, and more relevant recommendations.