Memorias de investigación
Artículos en revistas:
A collaborative filtering approach to mitigate the new user cold start problem
Año:2012
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommenda- tions received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system?s collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neu- ral learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave- one-out cross validation.
Internacional
Si
JCR del ISI
Si
Título de la revista
Knowledge-Based Systems
ISSN
0950-7051
Factor de impacto JCR
Información de impacto
Volumen
26
DOI
Número de revista
Desde la página
225
Hasta la página
238
Mes
SIN MES
Ranking
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: MERCATOR Tecnologías de la GeoInformación
  • Grupo de Investigación: Grupo de Sistemas Inteligentes
  • Departamento: Lenguajes, Proyectos y Sistemas Informáticos