WordRecommender: an explainable content-based algorithm based on sentiment analysis and semantic similarity (2020)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; PRESSATO, DIANY - ICMC ; ZANON, ANDRE LEVI - ICMC ; SOUZA, LUAN SOARES DE - ICMC
- Unidade: ICMC
- DOI: 10.1145/3428658.3431093
- Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL; RECONHECIMENTO DE TEXTO; ALGORITMOS ÚTEIS E ESPECÍFICOS
- Keywords: Recommender Systems; Content-Based Recommender Systems; Explanation; Sentiment Analysis; Natural Language Processing
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Proceedings
- Conference titles: Brazilian Symposium on Multimedia and the Web - WebMedia
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ZANON, André Levi et al. WordRecommender: an explainable content-based algorithm based on sentiment analysis and semantic similarity. 2020, Anais.. New York: ACM, 2020. Disponível em: https://doi.org/10.1145/3428658.3431093. Acesso em: 28 abr. 2024. -
APA
Zanon, A. L., Souza, L. S. de, Pressato, D., & Manzato, M. G. (2020). WordRecommender: an explainable content-based algorithm based on sentiment analysis and semantic similarity. In Proceedings. New York: ACM. doi:10.1145/3428658.3431093 -
NLM
Zanon AL, Souza LS de, Pressato D, Manzato MG. WordRecommender: an explainable content-based algorithm based on sentiment analysis and semantic similarity [Internet]. Proceedings. 2020 ;[citado 2024 abr. 28 ] Available from: https://doi.org/10.1145/3428658.3431093 -
Vancouver
Zanon AL, Souza LS de, Pressato D, Manzato MG. WordRecommender: an explainable content-based algorithm based on sentiment analysis and semantic similarity [Internet]. Proceedings. 2020 ;[citado 2024 abr. 28 ] Available from: https://doi.org/10.1145/3428658.3431093 - A user study with aspect-based sentiment analysis for similarity of items in content-based recommendations
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- A user study on explanations with different levels of detail in recommender systems
- Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data
- Personalizing explanations in recommender systems with different levels of details based on users\' reviews
- 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
Informações sobre o DOI: 10.1145/3428658.3431093 (Fonte: oaDOI API)
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