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

Bobadilla Sancho, Jesus and Ortega Requena, Fernando and Hernando Esteban, Antonio and 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.

Description

Title: A collaborative filtering approach to mitigate the new user cold start problem.
Author/s:
  • Bobadilla Sancho, Jesus
  • Ortega Requena, Fernando
  • Hernando Esteban, Antonio
  • Bernal Bermúdez, Jesús
Item Type: Article
Título de Revista/Publicación: Knowledge-Based Systems
Date: February 2012
ISSN: 0950-7051
Volume: 26
Subjects:
Freetext Keywords: Cold start, Recommender systems, Collaborative filtering Neural learning, Similarity measures, Leave-one-out-cross validation.
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

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.

More information

Item ID: 15302
DC Identifier: http://oa.upm.es/15302/
OAI Identifier: oai:oa.upm.es:15302
Official URL: http://www.journals.elsevier.com/knowledge-based-systems/
Deposited by: Memoria Investigacion
Deposited on: 25 Sep 2013 13:01
Last Modified: 21 Apr 2016 15:21
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