Model-agnostic knowledge graph embedding explanations for recommender systems (2024)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; ZANON, ANDRE LEVI - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-031-63797-1_1
- Subjects: SISTEMAS DE RECOMENDAÇÃO; ALGORITMOS ÚTEIS E ESPECÍFICOS
- Keywords: Model-agnostic explanation; Knowledge Graphs; Explanation Evaluation; Explainable AI
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Communications in Computer and Information Science
- ISSN: 1865-0929
- Volume/Número/Paginação/Ano: v. 2154, p. 3-27, 2024
- Conference titles: World Conference on Explainable Artificial Intelligence - xAI
- 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. Model-agnostic knowledge graph embedding explanations for recommender systems. Communications in Computer and Information Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-031-63797-1_1. Acesso em: 11 fev. 2026. , 2024 -
APA
Zanon, A. L., Rocha, L. C. D. da, & Manzato, M. G. (2024). Model-agnostic knowledge graph embedding explanations for recommender systems. Communications in Computer and Information Science. Cham: Springer. doi:10.1007/978-3-031-63797-1_1 -
NLM
Zanon AL, Rocha LCD da, Manzato MG. Model-agnostic knowledge graph embedding explanations for recommender systems [Internet]. Communications in Computer and Information Science. 2024 ; 2154 3-27.[citado 2026 fev. 11 ] Available from: https://doi.org/10.1007/978-3-031-63797-1_1 -
Vancouver
Zanon AL, Rocha LCD da, Manzato MG. Model-agnostic knowledge graph embedding explanations for recommender systems [Internet]. Communications in Computer and Information Science. 2024 ; 2154 3-27.[citado 2026 fev. 11 ] Available from: https://doi.org/10.1007/978-3-031-63797-1_1 - O impacto de estratégias de embeddings de grafos na explicabilidade de sistemas de recomendação
- Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data
- An exploration of recommender systems explanation paradigms: generating and evaluating syntactic, semantic, and generative models with knowledge graphs
- A user study with aspect-based sentiment analysis for similarity of items in content-based recommendations
- A multiturn recommender system with explanations
- WordRecommender: an explainable content-based algorithm based on sentiment analysis and semantic similarity
- 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
Informações sobre o DOI: 10.1007/978-3-031-63797-1_1 (Fonte: oaDOI API)
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