A user study on explanations with different levels of detail in recommender systems (2023)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; SOUZA, LUAN SOARES DE - ICMC
- Unidade: ICMC
- DOI: 10.1145/3617023.3617051
- Subjects: SISTEMAS DE RECOMENDAÇÃO; TRATAMENTO AUTOMÁTICO DE TEXTOS E DISCURSOS
- Keywords: Explanations; Text Summarization
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: Brazilian Symposium on Multimedia and the Web - WebMedia
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
SOUZA, Luan Soares de e MANZATO, Marcelo Garcia. A user study on explanations with different levels of detail in recommender systems. 2023, Anais.. New York: ACM, 2023. Disponível em: https://doi.org/10.1145/3617023.3617051. Acesso em: 23 jan. 2026. -
APA
Souza, L. S. de, & Manzato, M. G. (2023). A user study on explanations with different levels of detail in recommender systems. In Proceedings. New York: ACM. doi:10.1145/3617023.3617051 -
NLM
Souza LS de, Manzato MG. A user study on explanations with different levels of detail in recommender systems [Internet]. Proceedings. 2023 ;[citado 2026 jan. 23 ] Available from: https://doi.org/10.1145/3617023.3617051 -
Vancouver
Souza LS de, Manzato MG. A user study on explanations with different levels of detail in recommender systems [Internet]. Proceedings. 2023 ;[citado 2026 jan. 23 ] Available from: https://doi.org/10.1145/3617023.3617051 - Aspect-based summarization: an approach with different levels of details to explain recommendations
- Towards personality-aware explanations for music recommendations using generative AI
- Personalizing explanations in recommender systems with different levels of details based on users\' reviews
- A multiturn recommender system with explanations
- WordRecommender: an explainable content-based algorithm based on sentiment analysis and semantic similarity
- A user study with aspect-based sentiment analysis for similarity of items in content-based recommendations
- 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
Informações sobre o DOI: 10.1145/3617023.3617051 (Fonte: oaDOI API)
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