Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data (2022)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; ZANON, ANDRE LEVI - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.knosys.2022.109333
- Subjects: RECONHECIMENTO DE TEXTO; ALGORITMOS ÚTEIS E ESPECÍFICOS; WEB SEMÂNTICA
- Keywords: Recommender systems; Collaborative filtering; Linked open data; Explainable AI
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Knowledge-Based Systems
- ISSN: 0950-7051
- Volume/Número/Paginação/Ano: v. 252, p. 1-19, Sep. 2022
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ZANON, André Levi e ROCHA, Leonardo Chaves Dutra da e MANZATO, Marcelo Garcia. Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data. Knowledge-Based Systems, v. 252, p. Se 2022, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2022.109333. Acesso em: 20 maio 2024. -
APA
Zanon, A. L., Rocha, L. C. D. da, & Manzato, M. G. (2022). Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data. Knowledge-Based Systems, 252, Se 2022. doi:10.1016/j.knosys.2022.109333 -
NLM
Zanon AL, Rocha LCD da, Manzato MG. Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data [Internet]. Knowledge-Based Systems. 2022 ; 252 Se 2022.[citado 2024 maio 20 ] Available from: https://doi.org/10.1016/j.knosys.2022.109333 -
Vancouver
Zanon AL, Rocha LCD da, Manzato MG. Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data [Internet]. Knowledge-Based Systems. 2022 ; 252 Se 2022.[citado 2024 maio 20 ] Available from: https://doi.org/10.1016/j.knosys.2022.109333 - A multiturn recommender system with explanations
- A user study with aspect-based sentiment analysis for similarity of items in content-based recommendations
- WordRecommender: an explainable content-based algorithm based on sentiment analysis and semantic similarity
- Incorporating semantic item representations to soften the cold start problem
- Uma arquitetura de personalização de conteúdo baseada em anotações do usuário
- Similarity-based matrix factorization for item cold-start in recommender systems
- Combining multiple metadata types in movies recommendation using ensemble algorithms
- Exploiting multimodal interactions in recommender systems with ensemble algorithms
- Case recommender: a recommender framework
- Extended recommendation-by-explanation
Informações sobre o DOI: 10.1016/j.knosys.2022.109333 (Fonte: oaDOI API)
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