Can ofline metrics measure explanation goals?: A comparative survey analysis of ofline explanation metrics in recommender systems (2025)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1145/3779420
- Assunto: SISTEMAS DE RECOMENDAÇÃO
- Keywords: Explainability; Recommendation explanation; Evaluation; Recommendation evaluation
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: ACM Transactions on Recommender Systems - TORS
- ISSN: 2770-6699
- Volume/Número/Paginação/Ano: In press
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
ZANON, André Levi e ROCHA, Leonardo Chaves Dutra da e MANZATO, Marcelo Garcia. Can ofline metrics measure explanation goals?: A comparative survey analysis of ofline explanation metrics in recommender systems. ACM Transactions on Recommender Systems - TORS, 2025Tradução . . Disponível em: https://doi.org/10.1145/3779420. Acesso em: 28 fev. 2026. -
APA
Zanon, A. L., Rocha, L. C. D. da, & Manzato, M. G. (2025). Can ofline metrics measure explanation goals?: A comparative survey analysis of ofline explanation metrics in recommender systems. ACM Transactions on Recommender Systems - TORS. doi:10.1145/3779420 -
NLM
Zanon AL, Rocha LCD da, Manzato MG. Can ofline metrics measure explanation goals?: A comparative survey analysis of ofline explanation metrics in recommender systems [Internet]. ACM Transactions on Recommender Systems - TORS. 2025 ;[citado 2026 fev. 28 ] Available from: https://doi.org/10.1145/3779420 -
Vancouver
Zanon AL, Rocha LCD da, Manzato MG. Can ofline metrics measure explanation goals?: A comparative survey analysis of ofline explanation metrics in recommender systems [Internet]. ACM Transactions on Recommender Systems - TORS. 2025 ;[citado 2026 fev. 28 ] Available from: https://doi.org/10.1145/3779420 - 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
Informações sobre o DOI: 10.1145/3779420 (Fonte: oaDOI API)
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