Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback (2014)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; REZENDE, SOLANGE OLIVEIRA - ICMC
- Unidade: ICMC
- DOI: 10.1109/ICPR.2014.635
- Subjects: INTELIGÊNCIA ARTIFICIAL; WORLD WIDE WEB; SISTEMAS MULTIMÍDIA; RECUPERAÇÃO DA INFORMAÇÃO
- Language: Inglês
- Imprenta:
- Publisher: Conference Publishing Services
- Publisher place: Los Alamitos
- Date published: 2014
- Source:
- Título: Proceedings
- ISSN: 1051-4651
- Conference titles: International Conference on Pattern Recognition - ICPR
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
MANZATO, Marcelo Garcia et al. Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/ICPR.2014.635. Acesso em: 01 jan. 2026. -
APA
Manzato, M. G., Domingues, M. A., Marcacini, R. M., & Rezende, S. O. (2014). Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/ICPR.2014.635 -
NLM
Manzato MG, Domingues MA, Marcacini RM, Rezende SO. Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback [Internet]. Proceedings. 2014 ;[citado 2026 jan. 01 ] Available from: https://doi.org/10.1109/ICPR.2014.635 -
Vancouver
Manzato MG, Domingues MA, Marcacini RM, Rezende SO. Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback [Internet]. Proceedings. 2014 ;[citado 2026 jan. 01 ] Available from: https://doi.org/10.1109/ICPR.2014.635 - Improving personalized ranking in recommender systems with multimodal interactions
- Optimizing personalized ranking in recommender systems with metadata awareness
- Using contextual information from topic hierarchies to improve context-aware recommender systems
- Exploiting text mining techniques for contextual recommendations
- Generating recommendations based on robust term extraction from users' reviews
- Mining unstructured content for recommender systems: an ensemble approach
- Applying multi-view based metadata in personalized ranking for recommender systems
- 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
Informações sobre o DOI: 10.1109/ICPR.2014.635 (Fonte: oaDOI API)
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