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Fuzz-CBA: Classification à base de règles d'association floues et systèmes de recommandation

Joel Pinho Lucas 1, 2 Anne Laurent 3 Maria Moreno 2 Maguelonne Teisseire 3
1 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 Departamento de Informatica y Automatica
3 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Recommender systems are more and more used, especially on the Internet (e.g. movie and hotel recommandation). These systems often rely on classification methods, especially on association rule-based methods. However, the methods based on association rules and sequential patterns have not been studied for recommender systems in the case of numerical attributes. We thus propose an original recommendation method based on fuzzy association rules and show its interest through experiments.
Keywords : Recommendation Systems Rule-Base Classifiers Data Mining Fuzzy Association Rules
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Submitted on : Monday, October 7, 2019 - 3:50:30 PM
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  • HAL Id : lirmm-00430509, version 1

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LIRMM | CNRS | AGROPOLIS | MIPS | UNIV-MONTPELLIER

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Joel Pinho Lucas, Anne Laurent, Maria Moreno, Maguelonne Teisseire. Fuzz-CBA: Classification à base de règles d'association floues et systèmes de recommandation. LFA: Logique Floue et ses Applications, Sep 2009, Annecy, France. pp.283-290. ⟨lirmm-00430509⟩

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