Artículos en revistas:
A collaborative filtering similarity measure based on singularities
Año:2012
Áreas de investigación
- Sistema informático,
- Tratamiento de datos
Datos
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Descripción
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| Recommender systems play an important role in reducing the negative impact of informa- tion overload on those websites where users have the possibility of voting for their prefer- ences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to cal- culate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Information Processing & Management |
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ISSN
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0306-4573 |
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Factor de impacto JCR
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1,673 |
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Información de impacto
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Datos JCR del año 2010 |
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Volumen
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48 |
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DOI
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Número de revista
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2 |
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Desde la página
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204 |
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Hasta la página
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217 |
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Mes
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SIN MES |
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Ranking
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Participantes
- Autor: Fernando Ortega Requena UPM
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
- Creador: Grupo de Investigación: MERCATOR Tecnologías de la GeoInformación
- Departamento: Lenguajes, Proyectos y Sistemas Informáticos