Evaluating multiple user interactions for ranking personalization using ensemble methods (2018)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.18293/SEKE2018-0112
- Subjects: WORLD WIDE WEB; RECONHECIMENTO DE PADRÕES; SISTEMAS DE INFORMAÇÃO
- Keywords: recommender system; multimodal; user interaction
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: KSI Research Inc. and Knowledge Systems Institute Graduate School
- Publisher place: Pittsburgh
- Date published: 2018
- Source:
- Título: Proceedings
- ISSN: 2325-9000
- Conference titles: International Conference on Software Engineering & Knowledge Engineering- SEKE
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
DURÃO, Frederico Araujo et al. Evaluating multiple user interactions for ranking personalization using ensemble methods. 2018, Anais.. Pittsburgh: KSI Research Inc. and Knowledge Systems Institute Graduate School, 2018. Disponível em: https://doi.org/10.18293/SEKE2018-0112. Acesso em: 10 out. 2025. -
APA
Durão, F. A., Cabral, B. S., Beltrão, R. D., & Manzato, M. G. (2018). Evaluating multiple user interactions for ranking personalization using ensemble methods. In Proceedings. Pittsburgh: KSI Research Inc. and Knowledge Systems Institute Graduate School. doi:10.18293/SEKE2018-0112 -
NLM
Durão FA, Cabral BS, Beltrão RD, Manzato MG. Evaluating multiple user interactions for ranking personalization using ensemble methods [Internet]. Proceedings. 2018 ;[citado 2025 out. 10 ] Available from: https://doi.org/10.18293/SEKE2018-0112 -
Vancouver
Durão FA, Cabral BS, Beltrão RD, Manzato MG. Evaluating multiple user interactions for ranking personalization using ensemble methods [Internet]. Proceedings. 2018 ;[citado 2025 out. 10 ] Available from: https://doi.org/10.18293/SEKE2018-0112 - Uma arquitetura de personalização de conteúdo baseada em anotações do usuário
- Incorporating semantic item representations to soften the cold start problem
- A sentiment-based item description approach for kNN collaborative filtering
- 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
- Case recommender: a recommender framework
- Exploiting multimodal interactions in recommender systems with ensemble algorithms
- Similarity-based matrix factorization for item cold-start in recommender systems
Informações sobre o DOI: 10.18293/SEKE2018-0112 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
