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Descripción
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| A significant number of recommender systems utilize the k-nearest neighbor (kNN) algorithm as the collaborative filtering core. This algorithm is simple; it utilizes updated data and facilitates the explanations of recommendations. Its greatest inconveniences are the amount of execution time that is required and the non-scalable nature of the algorithm. The algorithm is based on the repetitive execution of the selected similarity metric. In this paper, an innovative similarity metric is presented: HwSimilarity. This metric attains high-quality recommendations that are similar to those provided by the best existing metrics and can be processed by employing low-cost hardware circuits. This paper examines the key design concepts and recommendation-quality results of the metric. The hardware design, cost of implementation, and improvements achieved during execution are also explored. | |
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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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Knowledge-Based Systems |
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ISSN
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0950-7051 |
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Factor de impacto JCR
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4,104 |
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Información de impacto
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Volumen
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51 |
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DOI
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http://dx.doi.org/10.1016/j.knosys.2013.06.010 |
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Número de revista
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51 |
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Desde la página
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27 |
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Hasta la página
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34 |
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Mes
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SIN MES |
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Ranking
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6/114 Artificial Intelligence |