A Balanced Memory-Based Collaborative Filtering Similarity Measure.

Bobadilla Sancho, Jesus and Ortega Requena, Fernando and Hernando Esteban, Antonio and Arroyo Castillo, Angel (2012). A Balanced Memory-Based Collaborative Filtering Similarity Measure.. "International Journal of Intelligent Systems", v. 27 ; pp. 939-946. ISSN 0884-8173. https://doi.org/10.1002/int.21556.

Description

Title: A Balanced Memory-Based Collaborative Filtering Similarity Measure.
Author/s:
  • Bobadilla Sancho, Jesus
  • Ortega Requena, Fernando
  • Hernando Esteban, Antonio
  • Arroyo Castillo, Angel
Item Type: Article
Título de Revista/Publicación: International Journal of Intelligent Systems
Date: 2012
ISSN: 0884-8173
Volume: 27
Subjects:
Faculty: E.U. de Informática (UPM)
Department: Sistemas Inteligentes Aplicados [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures.

More information

Item ID: 15303
DC Identifier: http://oa.upm.es/15303/
OAI Identifier: oai:oa.upm.es:15303
DOI: 10.1002/int.21556
Official URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1098-111X
Deposited by: Memoria Investigacion
Deposited on: 02 Oct 2013 11:41
Last Modified: 21 Apr 2016 15:21
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