Pendant longtemps, les distributeurs se sont intéressés au comportement d'achat intercatégoriel de leurs clients. Récemment, la recherche de modèles de relations intercatégorielles parmi les assortiments de distribution est devenue de plus en plus attractive, du fait de son potentiel promotionnel, pour les systèmes de recommandation utilisés dans des environnements Internet. Les algorithmes de filtrage collaboratif sont fréquemment utilisés dans de tels cadres pour la prédiction de choix, de préférences et/ou de notations d'internautes. Cet article étudie la pertinence de telles méthodes pour des situations de consommation où l'information de choix est binaire sans contrainte (i.e., choix/non-choix d'items, comme dans le cas de données concernant des paniers d'achats). Nous présentons une extension des algorithmes de filtrage collaboratif pour ce type de situation et nous l'appliquons à une base de données concrète de transactions commerciales. La nouvelle méthode est comparée à d'autres algorithmes plus conventionnels et peut être considérée comme précise pour effectuer des prévisions.
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