Evaluating the impact of demographic data on a hybrid recommender model (2014)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; GOULARTE, RUDINEI - ICMC
- Unidade: ICMC
- Subjects: ENGENHARIA DE SOFTWARE; SISTEMAS DE INFORMAÇÃO; WORLD WIDE WEB; SISTEMAS MULTIMÍDIA
- Language: Inglês
- Imprenta:
- Source:
- Título: IADIS International Journal on WWW/Internet
- ISSN: 1645-7641
- Volume/Número/Paginação/Ano: v. 12, n. 2, p. 149-167, 2014
-
ABNT
SANTOS JUNIOR, Edson B e MANZATO, Marcelo Garcia e GOULARTE, Rudinei. Evaluating the impact of demographic data on a hybrid recommender model. IADIS International Journal on WWW/Internet, v. 12, n. 2, p. 149-167, 2014Tradução . . Disponível em: http://www.iadisportal.org/ijwi/papers/2014121210.pdf. Acesso em: 20 jan. 2026. -
APA
Santos Junior, E. B., Manzato, M. G., & Goularte, R. (2014). Evaluating the impact of demographic data on a hybrid recommender model. IADIS International Journal on WWW/Internet, 12( 2), 149-167. Recuperado de http://www.iadisportal.org/ijwi/papers/2014121210.pdf -
NLM
Santos Junior EB, Manzato MG, Goularte R. Evaluating the impact of demographic data on a hybrid recommender model [Internet]. IADIS International Journal on WWW/Internet. 2014 ; 12( 2): 149-167.[citado 2026 jan. 20 ] Available from: http://www.iadisportal.org/ijwi/papers/2014121210.pdf -
Vancouver
Santos Junior EB, Manzato MG, Goularte R. Evaluating the impact of demographic data on a hybrid recommender model [Internet]. IADIS International Journal on WWW/Internet. 2014 ; 12( 2): 149-167.[citado 2026 jan. 20 ] Available from: http://www.iadisportal.org/ijwi/papers/2014121210.pdf - A conceptual architecture with trust consensus to enhance group recommendations
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- Hybrid recommenders: incorporating metadata awareness into latent factor models
- Introducing the concept of "always–welcome recommendations"
- Personalized collaborative filtering: a neighborhood model based on contextual constraints
- Leveraging hybrid recommenders with multifaceted implicit feedback
- Automatic annotation of tagged content using predefined semantic concepts
- Accelerometers data interoperability: easing interactive applications development
- CoBaR: confidence-based recommender
- Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization
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