| Título: |
HYBRID RECOMMENDATION SYSTEM BASED ON COLLABORATIVE FILTERING AND FUZZY NUMBERS
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| Instituição: |
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| Autor(es): |
MIGUEL ANGELO GASPAR PINTO
RICARDO TANSCHEIT
MARLEY MARIA BERNARDES REBUZZI VELLASCO
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| Colaborador(es): |
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| Catalogação: |
08 11:10:20.000000/02/2013 |
| Tipo: |
PAPER
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Idioma(s): |
ENGLISH - UNITED STATES
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| Nota: |
© 2012 IEEE. Reprinted, with permission, IEEE WORLD CONGRESS ON COMPUTATIONAL
INTELLIGENCE, JUNE 2012. This material is posted here with permission of the IEEE. Such
permission of the IEEE does not in any way imply IEEE endorsement of any of Pontifícia
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protecting it.
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| Referência [en]: |
https://www.maxwell.vrac.puc-rio.br/eletricaonline/serieConsulta.php?strSecao=resultado&nrSeq=21145@2 |
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Resumo:
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Online retail stores face great challenges to
recommend products due to the size and sparsity of the
databases, as well as the variety of new users and items. As
current techniques, based on collaborative filtering, address those
issues with only partial success, the present paper proposes the
use of a hybrid system of recommendation in online stores. This
system makes use of collaborative filtering and of a fuzzy number
model based on marketing concepts. Experimental results show
that the proposed system presents great invariance to sparse
databases, which is of great value for retail companies.
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