Descripción
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Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures. | |
Internacional
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Si |
JCR del ISI
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Si |
Título de la revista
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International Journal of Intelligent Systems |
ISSN
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0884-8173 |
Factor de impacto JCR
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Información de impacto
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Volumen
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27 |
DOI
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Número de revista
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Desde la página
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939 |
Hasta la página
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946 |
Mes
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SIN MES |
Ranking
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