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Semantic Customers’ Segmentation

Abstract : Many approaches have been proposed to allow customers’ segmentation in retail sector. However, very few contributions exploit the existing semantics links that may exist between objects and resulting groups. The aim of this paper is to overcome this drawback by using semantic similarity measures (SSM) in customers’ segmentation to provide clusters based on product’ topology instead of numerical indicators usually used (i.e. monetary indicators). More precisely, we intend to show the main advantage of SSM with a product taxonomy in the retail field. Usually, traditional approaches consider as similar three customers buying respectively apple, orange and beer. However, human intuition tends to group customers who buy orange and apple because both are fruits. Our approach is defined to identify this kind of grouping through SSM and abstract concepts belonging to product taxonomy. Experiments are conducted on real data from a French Retailer store and show the relevance of the proposed approach.
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Submitted on : Monday, January 13, 2020 - 3:27:51 PM
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Jocelyn Poncelet, Pierre-Antoine Jean, François Trousset, Jacky Montmain. Semantic Customers’ Segmentation. INSCI 2019 - 6th International Conference on Internet Science, Dec 2019, Perpignan, France. pp.318-325, ⟨10.1007/978-3-030-34770-3_26⟩. ⟨hal-02437116⟩



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