Multi-objective optimization of explanation metrics in recommender systems with LLMs (2025)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1109/ICTAI66417.2025.00027
- Subjects: SISTEMAS DE RECOMENDAÇÃO; PROCESSAMENTO DE LINGUAGEM NATURAL; APRENDIZADO COMPUTACIONAL
- Keywords: Explainability; Recommendation explanation; Large Language Models
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Almitos
- Date published: 2025
- Source:
- Título: Proceedings
- Conference titles: IEEE International Conference on Tools with Artificial Intelligence - ICTAI
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
ZANON, André Levi e ROCHA, Leonardo Chaves Dutra da e MANZATO, Marcelo Garcia. Multi-objective optimization of explanation metrics in recommender systems with LLMs. 2025, Anais.. Los Almitos: IEEE, 2025. Disponível em: https://doi.org/10.1109/ICTAI66417.2025.00027. Acesso em: 20 mar. 2026. -
APA
Zanon, A. L., Rocha, L. C. D. da, & Manzato, M. G. (2025). Multi-objective optimization of explanation metrics in recommender systems with LLMs. In Proceedings. Los Almitos: IEEE. doi:10.1109/ICTAI66417.2025.00027 -
NLM
Zanon AL, Rocha LCD da, Manzato MG. Multi-objective optimization of explanation metrics in recommender systems with LLMs [Internet]. Proceedings. 2025 ;[citado 2026 mar. 20 ] Available from: https://doi.org/10.1109/ICTAI66417.2025.00027 -
Vancouver
Zanon AL, Rocha LCD da, Manzato MG. Multi-objective optimization of explanation metrics in recommender systems with LLMs [Internet]. Proceedings. 2025 ;[citado 2026 mar. 20 ] Available from: https://doi.org/10.1109/ICTAI66417.2025.00027 - Metadata in movies recommendation: a comparison among different approaches
- gSVD++: supporting implicit feedback on recommender systems with metadata awareness
- A collaborative filtering approach based on user's reviews
- Multimodal interactions in recommender systems: an ensembling approach
- Exploiting feature extraction techniques on users' reviews for movies recommendation
- Exploiting item representations for soft clustering recommendation
- Combining different metadata views for better recommendation accuracy
- CoBaR: confidence-based recommender
- Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization
- Personalized ranking of movies: evaluating different metadata types and recommendation strategies
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