Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization (2014)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1145/2664551.2664556
- Subjects: MULTIMÍDIA INTERATIVA; WORLD WIDE WEB; RECUPERAÇÃO DA INFORMAÇÃO
- Language: Inglês
- Imprenta:
- ISBN: 9781450332309
- 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
FORTES, Arthur e MANZATO, Marcelo Garcia. Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization. 2014, Anais.. New York: ACM, 2014. Disponível em: https://doi.org/10.1145/2664551.2664556. Acesso em: 24 abr. 2024. -
APA
Fortes, A., & Manzato, M. G. (2014). Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization. In Proceedings. New York: ACM. doi:10.1145/2664551.2664556 -
NLM
Fortes A, Manzato MG. Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization [Internet]. Proceedings. 2014 ;[citado 2024 abr. 24 ] Available from: https://doi.org/10.1145/2664551.2664556 -
Vancouver
Fortes A, Manzato MG. Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization [Internet]. Proceedings. 2014 ;[citado 2024 abr. 24 ] Available from: https://doi.org/10.1145/2664551.2664556 - 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
- gSVD++: supporting implicit feedback on recommender systems with metadata awareness
- Metadata in movies recommendation: a comparison among different approaches
- A collaborative filtering approach based on user's reviews
Informações sobre o DOI: 10.1145/2664551.2664556 (Fonte: oaDOI API)
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