Advertisement

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)

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.

Keywords

Customers segmentation Semantic clustering Semantic similarity measures Retail 

References

  1. 1.
    Berrah, L., Mauris, G., Montmain, J.: Monitoring the improvement of an overall industrial performance based on a Choquet integral aggregation. Int. J. Manag. Sci. OMEGA 36, 340–351 (2008)CrossRefGoogle Scholar
  2. 2.
    Griva, A., Bardaki, C., Pramatari, K., Papakiriakopoulos, D.: Retail business analytics: customer visit segmentation using market basket data. Expert Syst. Appl. 16(1), 1–16 (2018)CrossRefGoogle Scholar
  3. 3.
    Ching-Hsue, C., You-Shyang, C.: Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst. Appl. 36(3), 4176–4184 (2009)CrossRefGoogle Scholar
  4. 4.
    Chen, Y.L., Kuo, M.H., Wu, S.Y., Tang, K.: Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electron. Commer. Res. Appl. 8(5), 241–251 (2009)CrossRefGoogle Scholar
  5. 5.
    Khajvand, M., Zolfaghar, K., Ashoori, S., Alizadeh, S.: Estimating customer lifetime value based on RFM analysis of customer purchase behavior: case study. Procedia Comput. Sci. 3, 57–63 (2011)CrossRefGoogle Scholar
  6. 6.
    Lingras, P., Elagamy, A., Ammar, A., Elouedi, Z.: Iterative meta-clustering through granular hierarchy of supermarket customers and products. Inf. Sci. 257, 14–31 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: Semantic similarity from natural language and ontology analysis. Synthesis Lectures on Human Language Technologies (2015)Google Scholar
  8. 8.
    Harispe, S., Snchez, D., Ranwez, S., Janaqi, S., Montmain, J.: A framework for unifying ontology-based semantic similarity. Study in the biomedical domain. J. Biomed. Inform. 48, 38–53 (2014)CrossRefGoogle Scholar
  9. 9.
    Hong, T., Kim, E.: Segmenting customers in online stores based on factors that affect the customers intention to purchase. Expert Syst. Appl. 39(2), 2127–2131 (2012)CrossRefGoogle Scholar
  10. 10.
    Aeron, H., Kumar, A., Moorthy, J.: Data mining framework for customer lifetime value-based segmentation. Expert Syst. Appl. 19(1), 17–30 (2012)Google Scholar
  11. 11.
    Park, C.H., Park, Y.H., Schweidel, D.A.: A multi-category customer base analysis. Int. J. Res. Mark. 31(3), 266–279 (2014)CrossRefGoogle Scholar
  12. 12.
    Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: The Semantic Measures Library and Toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies. Bioinformatics 30, 740–742 (2014)CrossRefGoogle Scholar
  13. 13.
    Harispe, S., Imoussaten, A., Trousset, F., Montmain, J.: On the consideration of a bring-to-mind model for computing the Information Content of concepts defined into ontologies. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2015)Google Scholar
  14. 14.
    Kim, H.K., Kim, J.K., Chen, Q.Y.: A product network analysis for extending the market basket analysis. Expert Syst. Appl. 39(8), 7403–7410 (2012)CrossRefGoogle Scholar
  15. 15.
    lbadvi, A., Shahbazi, M.: A hybrid recommendation technique based on product category attributes. Expert Syst. Appl. 36(9), 11480–11488 (2009)Google Scholar
  16. 16.
    Pesquita, C., Faria, D., Bastos, H., Falcao, A., Couto, F.: Evaluating go-based semantic similarity measures. In: Proceedings of 10th Annual Bio-Ontologies Meeting, vol. 37, p. 38 (2007)Google Scholar
  17. 17.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of IJCAI 1995, pp. 448–453 (1995)Google Scholar
  18. 18.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133–138 (1994)Google Scholar
  19. 19.
    Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in WordNet. In: 16th European Conference on Artificial Intelligence, pp. 1–5 (2004)Google Scholar
  20. 20.
    Schlicker, A., Domingues, F.S., Rahnenfhrer, J., Lengauer, T.: A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinform. 7, 302 (2006)CrossRefGoogle Scholar
  21. 21.
    Murtagh, F.: Wards hierarchical agglomerative clustering method: which algorithms implement wards criterion? J. Classif. 31, 274–295 (2014)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. 22, 31–72 (2011)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Srikant, R., Agrawal, R.: Mining generalized association rules. Future Gener. Comput. Syst. 13, 161–180 (1997)CrossRefGoogle Scholar

Copyright information

© 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

Personalised recommendations