Semantic Customers’ Segmentation

  • Jocelyn PonceletEmail author
  • Pierre-Antoine JeanEmail author
  • François TroussetEmail author
  • Jacky MontmainEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11938)


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.


Customers segmentation Semantic clustering Semantic similarity measures Retail 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LGI2P - IMT Mines Als - Université de MontpellierAlèsFrance
  2. 2.TRF Retail - 116 Alle Norbert WienerNîmesFrance

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