Using contextual information from topic hierarchies to improve context-aware recommender systems (2014)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; REZENDE, SOLANGE OLIVEIRA - ICMC
- Unidade: ICMC
- DOI: 10.1109/ICPR.2014.620
- 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 acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
DOMINGUES, Marcos Aurélio et al. Using contextual information from topic hierarchies to improve context-aware recommender systems. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/ICPR.2014.620. Acesso em: 17 fev. 2026. -
APA
Domingues, M. A., Manzato, M. G., Marcacini, R. M., Sundermann, C. V., & Rezende, S. O. (2014). Using contextual information from topic hierarchies to improve context-aware recommender systems. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/ICPR.2014.620 -
NLM
Domingues MA, Manzato MG, Marcacini RM, Sundermann CV, Rezende SO. Using contextual information from topic hierarchies to improve context-aware recommender systems [Internet]. Proceedings. 2014 ;[citado 2026 fev. 17 ] Available from: https://doi.org/10.1109/ICPR.2014.620 -
Vancouver
Domingues MA, Manzato MG, Marcacini RM, Sundermann CV, Rezende SO. Using contextual information from topic hierarchies to improve context-aware recommender systems [Internet]. Proceedings. 2014 ;[citado 2026 fev. 17 ] Available from: https://doi.org/10.1109/ICPR.2014.620 - Improving personalized ranking in recommender systems with multimodal interactions
- Optimizing personalized ranking in recommender systems with metadata awareness
- Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback
- 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
- 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
Informações sobre o DOI: 10.1109/ICPR.2014.620 (Fonte: oaDOI API)
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