Article

Improving the scalability of recommender systems by clustering using genetic algorithms

Authors:

Olga Georgiou,

Nicolas TsapatsoulisAuthors Info & Claims

ICANN'10: Proceedings of the 20th international conference on Artificial neural networks: Part I

Pages 442 - 449

Published: 15 September 2010 Publication History

Abstract

It is on human nature to seek for recommendation before any purchase or service request. This trend increases along with the enormous information, products and services evolution, and becomes more and more challenging to create robust, and scalable recommender systems that are able to perform in real time. A popular approach for increasing the scalability and decreasing the time complexity of recommender systems, involves user clustering, based on their profiles and similarities. Cluster representatives make successful recommendations for the other cluster members; this way the complexity of recommendation depends only on cluster size. Although classic clustering methods have been often used, the requirements of user clustering in recommender systems, are quite different from the typical ones. In particular, there is no reason to create disjoint clusters or to enforce the partitioning of all the data. In order to eliminate these issues we propose a data clustering method that is based on genetic algorithms. We show, based on findings, that this method is faster and more accurate than classic clustering schemes. The use of clusters created, based on the proposed method, leads to significantly better recommendation quality.

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Cited By

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Mueller J(2017)Combining aspects of genetic algorithms with weighted recommender hybridizationProceedings of the 19th International Conference on Information Integration and Web-based Applications & Services10.1145/3151759.3151765(13-22)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.1145/3151759.3151765
Wang ZYu XFeng NWang Z(2014)An improved collaborative movie recommendation system using computational intelligenceJournal of Visual Languages and Computing10.1016/j.jvlc.2014.09.01125:6(667-675)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.jvlc.2014.09.011
Bilge APolat H(2013)A comparison of clustering-based privacy-preserving collaborative filtering schemesApplied Soft Computing10.1016/j.asoc.2012.11.04613:5(2478-2489)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1016/j.asoc.2012.11.046
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Improving the scalability of recommender systems by clustering using genetic algorithms
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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Published In

ICANN'10: Proceedings of the 20th international conference on Artificial neural networks: Part I

September 2010

587 pages

ISBN:3642158188

Editors:
Konstantinos Diamantaras
TEI of Thessaloniki, Department of Informatics, Sindos, Greece
,
Wlodek Duch
Nicolaus Copernicus University, School of Physics, Astronomy and Informatics, Department of Informatics, Torun, Poland
,
Lazaros S. Iliadis
Democritus University of Thrace, Department of Forestry and Management of the Environment and Natural Resources, Orestiada Thrace, Greece

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 15 September 2010

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Mueller J(2017)Combining aspects of genetic algorithms with weighted recommender hybridizationProceedings of the 19th International Conference on Information Integration and Web-based Applications & Services10.1145/3151759.3151765(13-22)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.1145/3151759.3151765
Wang ZYu XFeng NWang Z(2014)An improved collaborative movie recommendation system using computational intelligenceJournal of Visual Languages and Computing10.1016/j.jvlc.2014.09.01125:6(667-675)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.jvlc.2014.09.011
Bilge APolat H(2013)A comparison of clustering-based privacy-preserving collaborative filtering schemesApplied Soft Computing10.1016/j.asoc.2012.11.04613:5(2478-2489)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1016/j.asoc.2012.11.046
Boumaza ABrun AMoore J(2012)From neighbors to global neighbors in collaborative filteringProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330214(345-352)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330214
Boumaza ABrun AOssowski SLecca P(2012)Stochastic search for global neighbors selection in collaborative filteringProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2245322(232-237)Online publication date: 26-Mar-2012
https://dl.acm.org/doi/10.1145/2245276.2245322

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