Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
A similarity metric designed to speed up, using hardware, the recommender systems k-nearest neighbors algorithm
Año:2013
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
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.
Internacional
Si
JCR del ISI
Si
Título de la revista
Knowledge-Based Systems
ISSN
0950-7051
Factor de impacto JCR
4,104
Información de impacto
Volumen
51
DOI
http://dx.doi.org/10.1016/j.knosys.2013.06.010
Número de revista
51
Desde la página
27
Hasta la página
34
Mes
SIN MES
Ranking
6/114 Artificial Intelligence
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Jesus Bobadilla Sancho (UPM)
  • Autor: Fernando Ortega Requena (UPM)
  • Autor: Antonio Hernando Esteban (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Intelligent Systems for Social learning and Virtual Environments
  • Departamento: Lenguajes, Proyectos y Sistemas Informáticos
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