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                <term xml:lang="en">Inconsistency</term>
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                <term xml:lang="fr">Préférences</term>
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              <p>Gathering the preferences of a user in order to make correct recommendations becomes a difficult task when said user is uncertain of her choices. Using possibility theory as a means of modelling and detecting this uncertainty, we propose in this paper methods based on information fusion to make inferences despite observed inconsistencies due to user errors. While our approach is general, we illustrate its potential benefits on synthetic experiments using weighted averages as preference models.</p>
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              <p>Recueillir les préférences d'une utilisatrice afin de faire des recommandations correctes est une tâche difficile dès lors que cette utilisatrice est incertaine de ses choix. En utilisant la théorie des possibilités comme moyen de modéliser et de détecter cette incertitude, nous proposons dans cet article des méthodes basées sur la fusion d'informations dans le but d'inférer malgré les incohérences observées dues aux erreurs de l'utilisatrice. Bien que notre approche soit générale, nous illustrons ses avantages potentiels sur des expériences synthétiques avec des moyennes pondérées comme modèle de préférence.</p>
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