Incorporating semantic item representations to soften the cold start problem (2018)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1145/3243082.3243112
- Subjects: SISTEMAS DE INFORMAÇÃO; RECONHECIMENTO DE PADRÕES; MULTIMÍDIA INTERATIVA
- Keywords: Recommender systems; Item representation; cold-start
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: Brazilian Symposium on Multimedia and the Web - WebMedia
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
D'ADDIO, Rafael Martins et al. Incorporating semantic item representations to soften the cold start problem. 2018, Anais.. New York: ACM, 2018. Disponível em: https://doi.org/10.1145/3243082.3243112. Acesso em: 12 fev. 2026. -
APA
D'Addio, R. M., Fressato, E. P., Costa, A. F. da, & Manzato, M. G. (2018). Incorporating semantic item representations to soften the cold start problem. In Proceedings. New York: ACM. doi:10.1145/3243082.3243112 -
NLM
D'Addio RM, Fressato EP, Costa AF da, Manzato MG. Incorporating semantic item representations to soften the cold start problem [Internet]. Proceedings. 2018 ;[citado 2026 fev. 12 ] Available from: https://doi.org/10.1145/3243082.3243112 -
Vancouver
D'Addio RM, Fressato EP, Costa AF da, Manzato MG. Incorporating semantic item representations to soften the cold start problem [Internet]. Proceedings. 2018 ;[citado 2026 fev. 12 ] Available from: https://doi.org/10.1145/3243082.3243112 - 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
- Exploiting feature extraction techniques on users' reviews for movies recommendation
- Exploiting item representations for soft clustering recommendation
- Combining different metadata views for better recommendation accuracy
- CoBaR: confidence-based recommender
- Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization
- Personalized ranking of movies: evaluating different metadata types and recommendation strategies
Informações sobre o DOI: 10.1145/3243082.3243112 (Fonte: oaDOI API)
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