Towards a more comprehensive comparison of collaborative filtering algorithms

Cristina N. González-Caro, Maritza L. Calderón-Benavides, José J. De Pérez-Alcázar, Juan C. Garcáa-Díaz, Joaquin Delgado

Research output: Book / Book Chapter / ReportResearch Bookspeer-review

5 Scopus citations


The basic objective of a Collaborative Filtering (CF) algorithm is to suggest items to a particular user based on his/her preferences and users with similar interests. Although, there is an apparently strong demand for CF techniques, and many algorithms have been recently proposed, very few articles comparing these techniques can be found. Our paper is oriented towards the study of a sample of algorithms to representing differents stages in the evolutive process of CF. Experiments were conducted on two datasets with different characteristics, using two protocols and three evaluation metrics for the different algorithms. The results indicate that, in general, the Online-Learning (WMA, MWM) and the Support Vector Machines algorithms have a better performance that the other algorithms, on both datasets. Considering the amount of information, the less sparse such information is, the higher the coverage and accuracy of general models tend to be; however, the behavior under sparse data is closer to what is observed in a real system if we have in mind that users usually rate an amount of records much smaller than the total available.

Original languageEnglish
Title of host publicationString Processing and Information Retrieval - 9th International Symposium, SPIRE 2002, Proceedings
EditorsAlberto H. F. Laender, Arlindo L. Oliveira
PublisherSpringer Verlag
Number of pages6
ISBN (Print)3540441581, 9783540441588
StatePublished - 2002
Event9th International Symposium on String Processing and Information Retrieval, SPIRE 2002 - Lisbon, Portugal
Duration: 11 Sep 200213 Sep 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Symposium on String Processing and Information Retrieval, SPIRE 2002


  • Aspect model
  • Collaborative filtering
  • Dependency networks
  • Memory based models
  • Online learning
  • Support vector machines


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