A collaborative filtering approach to mitigate the new user cold start problem.

Bobadilla Sancho, Jesus; Ortega Requena, Fernando; Hernando Esteban, Antonio y Bernal Bermúdez, Jesús (2012). A collaborative filtering approach to mitigate the new user cold start problem.. "Knowledge-Based Systems", v. 26 ; pp. 225-238. ISSN 0950-7051.

Descripción

Título: A collaborative filtering approach to mitigate the new user cold start problem.
Autor/es:
  • Bobadilla Sancho, Jesus
  • Ortega Requena, Fernando
  • Hernando Esteban, Antonio
  • Bernal Bermúdez, Jesús
Tipo de Documento: Artículo
Título de Revista/Publicación: Knowledge-Based Systems
Fecha: Febrero 2012
Volumen: 26
Materias:
Palabras Clave Informales: Cold start, Recommender systems, Collaborative filtering Neural learning, Similarity measures, Leave-one-out-cross validation.
Escuela: E.U. de Informática (UPM) [antigua denominación]
Departamento: Sistemas Inteligentes Aplicados [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (16MB) | Vista Previa

Resumen

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.

Más información

ID de Registro: 15302
Identificador DC: http://oa.upm.es/15302/
Identificador OAI: oai:oa.upm.es:15302
URL Oficial: http://www.journals.elsevier.com/knowledge-based-systems/
Depositado por: Memoria Investigacion
Depositado el: 25 Sep 2013 13:01
Ultima Modificación: 21 Abr 2016 15:21
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM