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Descripción
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| In this paper we propose a collaborative filtering method designed to improve the current memory-based prediction times, without worsening and even improving the existing accuracy results. The accuracy improvement is achieved by combining the numerical relevance of the ratings with non-numerical information, based on the votes structure. The improvement of the prediction time is achieved by setting four actions: 1) Simplification of the similarity measure design, in order to minimize the necessary calculations, 2) Construction and maintenance of a model that simplifies the predictions processing, 3) Optimization of the computation, using a set-based model and a bit-based processing implementation, and 4) Switching between the bit processing and the numerical processing, depending on the density of the users? ratings. Experimental results show improvements both in the prediction time and the accuracy. Experiments have used a significant amount of state of the art baselines and collaborative filtering public datasets. | |
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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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Ieee Access |
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ISSN
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2169-3536 |
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Factor de impacto JCR
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3,244 |
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Información de impacto
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Datos JCR del año 2016 |
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Volumen
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DOI
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10.1109/ACCESS.2018.2868464 |
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Número de revista
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